{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<div style=\"text-align: center\"><font color='#dc2624' face='微软雅黑' size = \"6\">基础线性回归</font></div>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\" # 代码块显示所有执行结果\n",
    "import os \n",
    "os.chdir(\"D:/py_workdir1\") # 设置工作目录"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import font_manager"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<div style=\"text-align: left\"><font color='black' face='微软雅黑' size = \"6\">引言</font><a name='top'></a></div>**\n",
    "\n",
    "<p style=\"color:blue; font-size:150%; font-weight:bold\">概念辨析:</p>\n",
    "\n",
    "* 相对于微观量的统计平均性质的宏观量也叫**统计量**。<br/>\n",
    "* **RSS**: 残差平方和。<br/>\n",
    "* **F-statistic**: F统计量，用来检验原假设，原假设认为模型的所有系数都是0.<br/>\n",
    "* $R^2$, **拟合优度**，在0-1之间，表示模型对方差的解释占比，数值越大，则拟合程度就越大。\n",
    "只有在带有截距项的线性最小二乘多元回归中, $R^2$才等于数据实测值和方程拟合值的相关系数的平方。<br/>\n",
    "* `P-value`, P值，模型原假设是变量之间不存在显性的线性关系，如果结果存在显著的线性关系，则P值应该小于`0.05`。  \n",
    "* **优势比**, Odds ratio, 即**赔率**的比值，巴西队如果赢得世界杯的概率是20%, \n",
    "那么赔率就是$0.2/(1-0.2)=0.25$, 即1赔4，同样的，德国队的概率是25%, 那么赔率就是0.20即1赔5.\n",
    "所以巴西队与德国队的**优势比**就是$0.25/0.2=1.25$。<br/> \n",
    "* **ROC**图，受试者工作特征图，ROC图基于分类器的性能对其进行可视化、组织和选择。\n",
    "因此ROC图只适用于2个分类的分类器。\n",
    "在ROC图中，Y轴表示**真阳性率(TPR)**， X轴表示**假阳性率(FPR)**。<br/>\n",
    "* **AUC**, ROC曲线与X轴围成的面积，面积越大，则模型越表现越好。<br/>\n",
    "* **正确率**，Accuracy, 表示正确预测数量的比例，混淆矩阵正对角线数字相加，除以数字总和。<br/>\n",
    "* **准确率**, Precision, 表示正确识别阳性病例的比率。<br/>\n",
    "* **真阳性率**，TPR, 也称为**灵敏度**(Sensitivity)或召回率(Recall), 正确识别真阳性病例的比率。<br/>\n",
    "* **假阳性率**，FPR，表示错误识别真阴性病例的比率。<br/>\n",
    "* **特异度**，Specificity，也称为**真阴性率**(TNR), 表示正确识别真阴性病例的比率。$TNR=1-FPR$ <br/>\n",
    "\n",
    "\n",
    "**混淆矩阵计算表格：**  \n",
    "\n",
    "|  | 预测阳性 | 预测阴性 | \n",
    "| :-- | :--  | :-- |     \n",
    "| 实际阳性 | TP | FN | \n",
    "| 实际阴性 | FP | TN |\n",
    "\n",
    "* $TPR=\\frac {TP}{TP+FN}$ \n",
    "* 准确率=$\\frac {TP}{TP+FP}$ \n",
    "* $TNR=\\frac {TN}{FP + TN}$\n",
    "* 正确率=$\\frac {TP+TN}{TP+TN+FN+FP}$\n",
    "* **假阳性率**: $FPR=\\frac {FP}{FP+TN}$  \n",
    "* **Prevalence**: $\\frac {TP + FN}{TP+FN + TN + FP}$\n",
    "\n",
    "<p style=\"color:blue; font-size:150%; font-weight:bold\">模型诊断:</p>\n",
    "\n",
    "逻辑回归必须通过假设检验，  \n",
    "\n",
    "* **提升图**(lift chart), 提升图是衡量预测模型性能的可视化方法，<br/>\n",
    "    * x轴表示病例数量；<br/>\n",
    "    * y轴表示累积阳性的百分比；<br/>\n",
    "    * 对角线为参考基线；<br/>\n",
    "    * 提升曲线与基线分得越开，则模型越好。<br/>\n",
    "    * 同一x刻度对应的提升曲线与基线之差，就是实际提升，即模型预测结果和不使用模型的结果之差。<br/>\n",
    "* **ROC曲线**(A receiver operating characteristic)， ROC曲线是机器学习中最常见的模型比较可视化方法。 \n",
    "ROC曲线横坐标为**假阳性率**，纵坐标为**真阳性率**。 \n",
    "ROC曲线上升趋势越快，与横坐标围成区域面积越大，则模型性能越优异。\n",
    "ROC曲线与横坐标围成的面积成为AUC(Area Under the Curve), AUC应该落在0.5~1之间，即ROC曲线在对角线上方。 \n",
    "具有2个分类的模型最适应ROC方法。  \n",
    "* **Wald检验**(the Wald test)，Wald检验类似线性回归中的t检验, <br/>\n",
    "$$W_j = \\frac {\\beta_j^2}{{SE_{\\beta_j}^2}}$$ \n",
    "$\\beta$是逻辑回归系数，Wald统计量就是回归系数的平方与系数标准差的平方之比。它服从卡方分布。\n",
    "Wald检验原假设是模型中系数为0，如果p<0.05则表明模型中变量是显著的，即Wald检验通过。<br/>\n",
    "* **偏差**(deviance)检验, 逻辑回归模型的偏差根据空模型和饱和模型的似然估计计算得到。<br/>\n",
    "$$D=-2\\ln {\\frac {空模型的似然估计}{饱和模型的似然估计}}$$\n",
    "空模型指没有预测结果的模型（对训练集进行预测），而饱和模型是包含部分预测结果的模型。\n",
    "偏差统计量D服从卡方分布，数值越小，则拟合越好。  \n",
    "* **伪R方**(pseudo R-square), 线性回归中，有$R^2$指标，用来度量模型对方差的解释占比，对逻辑回归中，对应的是伪R方，最常用伪R方就是似然比（the likelihood ratio）。<br/>\n",
    "$${R_L^2} = \\frac {D_{null} - D_{fitted}}{D_{null}}$$\n",
    "它是空模型与包含预测结果的空模型的偏差之差，然后与空模型偏差的之比。\n",
    "数值越大，表示模型的解释能力越强。\n",
    "其它的伪R方还有：Cox，Snell R方， Nagelkerke R方， McFadden R方， Tjur R方。  \n",
    "* **双变量图**(Bivariate Plots), 逻辑回归中最重要的检验是查看各个自变量的各个级别的实际概率和预测概率。\n",
    "双变量图，就是指结果变量的实际值与预测值，在各个自变量级别中的变化趋势，这里有3个重要的输入：\n",
    "    * 实际概率(Actual Probability)， 结果变量实际值在各个自变量级别中的先验比例(prior proportion)。<br/>\n",
    "    * 预测概率(Predicted Probability), 结果变量预测值在各个自变量级别中的比例。<br/>\n",
    "    * 频率(Frequency), 各个自变量中各个级别的出现频次。<br/>\n",
    "\n",
    " 双变量图可以反应各个自变量各个级别对结果变量的影响，**每个自变量与结果变量对应1张双变量图**。<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>目录<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#一元线性回归\" data-toc-modified-id=\"一元线性回归-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>一元线性回归</a></span><ul class=\"toc-item\"><li><span><a href=\"#建模\" data-toc-modified-id=\"建模-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>建模</a></span></li><li><span><a href=\"#评价模型\" data-toc-modified-id=\"评价模型-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>评价模型</a></span></li><li><span><a href=\"#预测\" data-toc-modified-id=\"预测-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>预测</a></span></li></ul></li><li><span><a href=\"#多项式回归\" data-toc-modified-id=\"多项式回归-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>多项式回归</a></span><ul class=\"toc-item\"><li><span><a href=\"#建模\" data-toc-modified-id=\"建模-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>建模</a></span></li><li><span><a href=\"#评价模型\" data-toc-modified-id=\"评价模型-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>评价模型</a></span></li><li><span><a href=\"#预测\" data-toc-modified-id=\"预测-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>预测</a></span></li></ul></li><li><span><a href=\"#多元线性回归\" data-toc-modified-id=\"多元线性回归-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>多元线性回归</a></span><ul class=\"toc-item\"><li><span><a href=\"#糖尿病数据\" data-toc-modified-id=\"糖尿病数据-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>糖尿病数据</a></span><ul class=\"toc-item\"><li><span><a href=\"#数据探索\" data-toc-modified-id=\"数据探索-3.1.1\"><span class=\"toc-item-num\">3.1.1&nbsp;&nbsp;</span>数据探索</a></span></li><li><span><a href=\"#数据预处理\" data-toc-modified-id=\"数据预处理-3.1.2\"><span class=\"toc-item-num\">3.1.2&nbsp;&nbsp;</span>数据预处理</a></span></li><li><span><a href=\"#建模\" data-toc-modified-id=\"建模-3.1.3\"><span class=\"toc-item-num\">3.1.3&nbsp;&nbsp;</span>建模</a></span></li><li><span><a href=\"#预测\" data-toc-modified-id=\"预测-3.1.4\"><span class=\"toc-item-num\">3.1.4&nbsp;&nbsp;</span>预测</a></span></li><li><span><a href=\"#评价模型\" data-toc-modified-id=\"评价模型-3.1.5\"><span class=\"toc-item-num\">3.1.5&nbsp;&nbsp;</span>评价模型</a></span></li></ul></li></ul></li><li><span><a href=\"#逻辑回归\" data-toc-modified-id=\"逻辑回归-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>逻辑回归</a></span><ul class=\"toc-item\"><li><span><a href=\"#乳腺癌数据\" data-toc-modified-id=\"乳腺癌数据-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>乳腺癌数据</a></span><ul class=\"toc-item\"><li><span><a href=\"#数据探索\" data-toc-modified-id=\"数据探索-4.1.1\"><span class=\"toc-item-num\">4.1.1&nbsp;&nbsp;</span>数据探索</a></span></li><li><span><a href=\"#数据预处理\" data-toc-modified-id=\"数据预处理-4.1.2\"><span class=\"toc-item-num\">4.1.2&nbsp;&nbsp;</span>数据预处理</a></span><ul class=\"toc-item\"><li><span><a href=\"#缺失值插补\" data-toc-modified-id=\"缺失值插补-4.1.2.1\"><span class=\"toc-item-num\">4.1.2.1&nbsp;&nbsp;</span>缺失值插补</a></span></li><li><span><a href=\"#划分训练集和测试集\" data-toc-modified-id=\"划分训练集和测试集-4.1.2.2\"><span class=\"toc-item-num\">4.1.2.2&nbsp;&nbsp;</span>划分训练集和测试集</a></span></li><li><span><a href=\"#标准化\" data-toc-modified-id=\"标准化-4.1.2.3\"><span class=\"toc-item-num\">4.1.2.3&nbsp;&nbsp;</span>标准化</a></span></li></ul></li><li><span><a href=\"#建模\" data-toc-modified-id=\"建模-4.1.3\"><span class=\"toc-item-num\">4.1.3&nbsp;&nbsp;</span>建模</a></span></li><li><span><a href=\"#预测\" data-toc-modified-id=\"预测-4.1.4\"><span class=\"toc-item-num\">4.1.4&nbsp;&nbsp;</span>预测</a></span></li><li><span><a href=\"#评价模型\" data-toc-modified-id=\"评价模型-4.1.5\"><span class=\"toc-item-num\">4.1.5&nbsp;&nbsp;</span>评价模型</a></span></li></ul></li><li><span><a href=\"#前列腺数据\" data-toc-modified-id=\"前列腺数据-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>前列腺数据</a></span><ul class=\"toc-item\"><li><span><a href=\"#数据探索\" data-toc-modified-id=\"数据探索-4.2.1\"><span class=\"toc-item-num\">4.2.1&nbsp;&nbsp;</span>数据探索</a></span></li><li><span><a href=\"#数据预处理\" data-toc-modified-id=\"数据预处理-4.2.2\"><span class=\"toc-item-num\">4.2.2&nbsp;&nbsp;</span>数据预处理</a></span><ul class=\"toc-item\"><li><span><a href=\"#划分训练集与测试集\" data-toc-modified-id=\"划分训练集与测试集-4.2.2.1\"><span class=\"toc-item-num\">4.2.2.1&nbsp;&nbsp;</span>划分训练集与测试集</a></span></li><li><span><a href=\"#标准化\" data-toc-modified-id=\"标准化-4.2.2.2\"><span class=\"toc-item-num\">4.2.2.2&nbsp;&nbsp;</span>标准化</a></span></li></ul></li><li><span><a href=\"#建模\" data-toc-modified-id=\"建模-4.2.3\"><span class=\"toc-item-num\">4.2.3&nbsp;&nbsp;</span>建模</a></span></li><li><span><a href=\"#预测\" data-toc-modified-id=\"预测-4.2.4\"><span class=\"toc-item-num\">4.2.4&nbsp;&nbsp;</span>预测</a></span></li><li><span><a href=\"#评价模型\" data-toc-modified-id=\"评价模型-4.2.5\"><span class=\"toc-item-num\">4.2.5&nbsp;&nbsp;</span>评价模型</a></span></li></ul></li><li><span><a href=\"#糖尿病数据集\" data-toc-modified-id=\"糖尿病数据集-4.3\"><span class=\"toc-item-num\">4.3&nbsp;&nbsp;</span>糖尿病数据集</a></span><ul class=\"toc-item\"><li><span><a href=\"#数据探索\" data-toc-modified-id=\"数据探索-4.3.1\"><span class=\"toc-item-num\">4.3.1&nbsp;&nbsp;</span>数据探索</a></span></li><li><span><a href=\"#数据预处理\" data-toc-modified-id=\"数据预处理-4.3.2\"><span class=\"toc-item-num\">4.3.2&nbsp;&nbsp;</span>数据预处理</a></span><ul class=\"toc-item\"><li><span><a href=\"#标准化\" data-toc-modified-id=\"标准化-4.3.2.1\"><span class=\"toc-item-num\">4.3.2.1&nbsp;&nbsp;</span>标准化</a></span></li></ul></li><li><span><a href=\"#建模\" data-toc-modified-id=\"建模-4.3.3\"><span class=\"toc-item-num\">4.3.3&nbsp;&nbsp;</span>建模</a></span></li><li><span><a href=\"#预测\" data-toc-modified-id=\"预测-4.3.4\"><span class=\"toc-item-num\">4.3.4&nbsp;&nbsp;</span>预测</a></span></li><li><span><a href=\"#评价模型\" data-toc-modified-id=\"评价模型-4.3.5\"><span class=\"toc-item-num\">4.3.5&nbsp;&nbsp;</span>评价模型</a></span></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一元线性回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Htm3bIlVqQI0aNSIhIYEaNWowZcqUMvXx3929c2fg2ynKc8f7idTh17hxY2688UZuvPFGrLUsWLCAa665hieffJLx48eTnFw4+UyrVq24/fbbuf3228nNzWXatGlMmDCB3/72t4wdOzbklFDNmzcH4Lvvvgt4fMeOHQDF7n6vjMJ1TWZj4OwiH+BMKVR0X+Mi7RNKHKvr29+5yL7jJkW31r6Nc8p8AdANuAjYClxvrb0zTF+LiIiI6wYNGgTA7Nmzy9T+559/Jj8/n2bNmh0XMHNzc5k1a1aQnpERFxfHwIEDSU9PZ9myZWXq0717dxISElixYgW7dx+/1srMmYFWkg5/HYEYYxg2bBjDhw8HYNOmTSGf89prr6Vnz54cO3aMLVu2hHxs/9REb7zxRsDj/v2BpjCqTMISMq21U6y1ppSPKUXaby9D+/FBnutTa+2F1toG1tra1tqzrLWvhOPrEBER8co999xDYmIid999d8DJ2tPT0/n3v/9NVlYWAE2aNKF+/fps2rSJTz/9tKBdXl4e999/f6lBJxL+8Ic/EBMTw3XXXccnn3xy3PG9e/fyr3/9q+DzunXrMmbMGHJzc7n77rs5duxYwbElS5YwdepUV+pYu3Yt77zzDjk5OcXaHTx4kJUrVwKF139+/PHHfPjhh+Tn5xdru2PHDjZv3owxJuh1k35XX301TZs2Zfny5UyePLnYseeff57Vq1fTsmVLLrvssrJ/0VGoMl2TKSIiUmWddtppvP7661x77bVcfPHFdOzYkdNPPx1rLTt27CAtLY1jx44xZswYEhMTiYuL4/777+fBBx9kwIABnH/++TRs2JCVK1fyww8/cOuttxYLUm4499xzee6557jrrrvo378/KSkpnHbaaRw9erQghNWpU6fYNYuPP/44S5YsYebMmaxatYq+ffvy448/smTJEn7zm9/wwgsvFEzzFKk6duzYwRVXXEH9+vU566yzOPnkkzl06BDLly/nl19+4bLLLiu4+Wf9+vXcfffdNG7cmB49epCUlMRPP/3EsmXLOHr0KHfddVfB6fBgateuzRtvvMHIkSO5+eabmTx5Mh06dOCrr75i7dq11K5dmzfffLPYlE6VUWWawkhERKRKu/zyy1m/fj0333wzOTk5zJs3jyVLlpCdnc3YsWOZM2cO9esX3sT0hz/8gVdffZWUlBQ+/fRTPvzwQ8444wxWrFjBWWed5cnXcNttt7Fy5UrGjh3LwYMHee+99/j888+JiYnhlltu4d133y3WvkmTJnz++edcf/31ZGVlMXv2bA4cOMArr7xSMEdkUlJSROvo3bs3jz76KD169ODrr79mxowZfPHFF6SkpPDqq68yffr0grYjRozgoYceokOHDqxfv54ZM2aQmppK//79mTVrFs8880yZ6rvgggtYvXo1o0ePZvfu3cycOZN9+/Zx7bXX8uWXX1b6U+UAprQ7t6obY0xqcnJycmpqqteliIi4Lj8/v2Dd5I4dOx432bSIm5544gkeeOABHn/8cX73u995XU6VUN6f8c6dO5OWlpZmgyzHHYrePURERMRTa9asOW7fsmXLeOyxx4iLi+Pqq6/2oCo5UbomU0RERDzVt29fmjdvzumnn07t2rX59ttvWbt2LeBcsxlo6UyJfgqZIiIi4qnf//73fPDBB6xcuZKff/6ZevXqMWTIEG677baA665L5aCQKSIiIp56+OGHefjhh70uQ8JM12SKiIiISNgpZIqIiIhI2ClkioiIiEjYKWSKiIiISNgpZIqIiIhI2ClkioiIiEjYKWSKiIiISNgpZIqIiIhI2ClkioiIiEjYKWSKiIh4ZPz48RhjWLJkSVQ9VmWuQaKHQqaIiIhElSVLlmCMYfz48V6XIidAIVNERMQjf/vb39i8eTO9evWKqscSCYc4rwsQEZHq62BGNvPW7mTjznSysnNJTIgjpU0Sw7q1okGdBK/Li7hmzZrRrFmzqHsskXDQSKaIiLguOyePZ+ds4NrnFvPqki2s2bafzXsOsWbbfqZ8/DXXPreY5z7YyLHcPK9LLebzzz/nkksuoXHjxiQkJNC2bVt+85vfsHfv3uPaTpkyBWMMjzzyCFu2bGHUqFE0bdqUmJgYZs+eDYS+hnHt2rVceOGF1K9fn/r16zN06FBWr15d7HGLCvZYbdu2xRgDwEsvvURKSgqJiYmcfPLJ3HzzzRw6dKjcr8Pbb79Nr169SExMpGnTpowbNy7ga+C3fPlybrvtNlJSUmjQoAGJiYl06tSJBx544LjnHz9+POeddx4Ar776KsaYgo+iX/MHH3zA9ddfz+mnn069evWoXbs2Z5xxBo899hjZ2dnl/pok/DSSKSIirsrOyeOhqavYsCM9aJvcfMvcNTvZfSCDR0f3IqFGrIsVBvb6668zfvx48vPz6du3L61atWLNmjVMmjSJd955hyVLltCpU6fj+n399df07NmTpKQkzjvvPA4ePEiNGjVCPtdnn33GoEGDyMrK4swzz6Rjx46kpaXRr18/JkyYUKH677//fp577jl69uzJsGHD+Oyzz5g8eTKbN29m6dKlBUG0NC+88AK33347sbGxDBgwgEaNGvHhhx/Su3dvzjjjjIB97rvvPtatW0eXLl04//zzyc7OZs2aNTzxxBPMmTOHFStWUKdOHQD69evHvn37WLBgAe3ataNfv34Fj9OtW7eCf99www1kZmbSuXNnunbtyi+//MKqVat48MEHWbx4MQsXLiQ21vvvm+pMIVNERFw1aUFqyIBZ1IYd6by4MI07h3eNcFWh7dq1i5tuugljDO+99x4jRowAID8/n3vuuYdnn32WcePGsWrVquP6Tps2jdtuu41nn322TKEnPz+fCRMmkJWVxd///nfuu+++gmN/+ctf+OMf/1ihr+H1119n5cqVBUFt//799OnTh+XLl/Pxxx9z/vnnl/oY27dv59577yUhIYH58+czcOBAAI4cOcKll17KnDlzAvb74x//SJ8+fWjQoEHBvuzsbO644w4mT57M008/XfB13XjjjbRv354FCxbQr18/pkyZEvAxX3zxRQYPHkzt2rUL9h0+fJgxY8YwZ84c3njjDcaNG1eGV0YiRafLRUTENekZR1m0fne5+ixct4uDGd6e/nzppZfIyspi9OjRBQETICYmhscff5zmzZuzevVqVqxYcVzfxo0b88QTT5R5VO2jjz5iy5YtdOrUiXvvvbfYsT/84Q+ccsopFfoa/vKXvxQbCWzUqBETJ04EYNmyZWV6jP/9739kZ2czbty4goAJUKtWLf75z38GHQ296KKLigVMgISEBJ599lni4uJ49913y/fFAJdeemmxgAlQt25dnnnmGYAKPaaEl0YyRUTENfPX7iI335arT26+Zf66XYzu1z5CVZVu+fLlAIwdO/a4YwkJCVx11VU899xzLF++nN69exc7PmjQIGrVqlXm5/rss88AuPLKK48LbbGxsVx++eU89dRT5f0SGDJkyHH7OnToAMD3339fpsf45JNPALj66quPO9axY0fOPPNM1qxZE7Dvnj17eP/99/nqq6/45ZdfyM/PByA+Pp5vvvmmTM9f0jfffMPcuXP59ttvyczMJD8/H2ttwTHxlkKmiIi4ZuPOsp0mL2nDjgOehkz/TS1t27YNeNy/P9DNL61bt67Qc7Vq1Srg8fI+nl/Lli2P2+e/DrKsN8r4awtWQ+vWrQOGzKeffprf//73HDt2rKzlhmSt5d577+WZZ54pCJUlHT58OCzPJRWn0+UiIuKarOxcV/uFW2k3xwQ6XrNmzbA+V7BQVdHHKw//c5fnsVasWME999xDYmIiU6ZMYfv27Rw9ehRrLdbaCk279NZbb/H000/TokULZs6cyZ49ezh27BjW2oLAXNHXScJHIVNERFyTmFCxE2gV7RcuzZs3B+C7774LeHzHjh0AYZmn0v8YO3fuDHh8165dJ/wcFeV/Hfxfb0mBap41axYAjz76KNdddx1t2rQhIcGZAzUrK4t9+/aVuw7/Y06aNIkrrriC5s2bF9yxv23btnI/nkSGQqaIiLima+uGFeqX0iYpzJWUT//+/QF44403jjt27NgxZsyYUazdiejbty/gzEVZcjQuPz+/IGB5wT+dkP/rLWrLli2sW7fuuP0HDx4EAp/+nzFjRsARx/j4eABycwOPYId6zOnTpwepXtymkCkiIq4ZdmYr4mLKd9o2LsYwrFvg6xPdcsMNN5CYmMjUqVP54IMPCvbn5+fzhz/8gT179tCzZ8/jbvqpiPPPP5/27duzefPmgjul/R5//HFPR+omTJhAfHw8r732WsHNUOCMSN55550FN/MU5b+56OWXXyYnJ6dgf1paGr/73e8CPo9/xPTrr78OeNz/mJMnTy4WUpcvX86TTz5Zzq9KIkUhU0REXNOwTk0Gn3H8DSihDImCJSZbt25dEGhGjhxJ//79GTNmDMnJyTz11FM0bdqU1157LSzPFRsbyyuvvELNmjW555576NGjB2PGjKFbt2786U9/4te//jVQONrnplNPPZUnnniCo0ePct555zFo0CBGjRpF+/bt2bRpU7HpnfwmTJjAySefzPvvv0/Hjh255pprGDx4MN26daN///60adPmuD5t27YlJSWFL774gl69ejFhwgRuvPFG3nvvPQDuuOMOateuzb///W+6dOnC6NGjOffccxkwYAC33HJLxF8HKRuFTBERcdXEoZ1JaVO20+YpbRoycWhyhCsqm2uvvZZly5YxYsQINm/ezMyZM8nKymLixIl8+eWXAVf7qah+/frxySefMHToUL755hvmzJlDo0aNWLp0aUEoS0ry5hKCu+66i+nTp9OtWzc++eQTFi9ezMCBA1mxYkXAmpKSkli9ejVjxozh2LFjvPfee+zZs4c///nPTJ06NejzvP3221x66aVs27aN1157jZdffrngzvUOHTqwevVqRo4cyf79+3nvvffIyMjgP//5j0Yyo4jR3VfFGWNSk5OTk1NTU70uRUTEdfn5+QWnKDt27EhMTGTGIrJz8nhxYRoL1wWeNzMuxjCkWysmDk0mPk5LAxZ14YUXMn/+fFasWMHZZ5/tdTlSyZT3Z7xz586kpaWlWWs7l/e5NE+miIi4LqFGLHcO78q4AR2Yv24XG3YcICs7l8SEOFLaJDEsCk6Reyk9PZ3Dhw8XO5VsreWFF15g/vz5tG/fnl69enlYoUjpFDJFRMQzDeokMLpfe08nWo9GW7ZsoW/fvqSkpHDqqaeSl5fHpk2b2LZtG4mJifz3v/8Ny7yXIpGkazJFRESizKmnnsott9xCdnY2ixcvZt68eWRnZzNmzBhWrlxZbN1wkWilkUwREZEo06RJE/797397XYbICdFIpoiIiIiEnUKmiIiIiISdQqaIiIiIhJ1CpoiIFCh6x3KgJQJFpHIr+nMd6RkKdOOPBw5mZDNv7U427kzXvHAiElWMMcTHx3Ps2DEyMzOpX7++1yWJSBhlZmYCzrKkCplVSHZOHpMWpLJo/e7jVrhYs20/ry/dohUuRMRzdevW5cCBA/zwww8A1K5dO2Ir/4iIO/Lz88nMzCz4ua5bt27En1Mh0yXZOXk8NHUVG3akB22Tm2+Zu2Ynuw9k8OjoXiTUUNAUEfclJSWRmZnJ0aNH2bt3r9fliEiY1axZM+A68+GmP01dMmlBasiAWdSGHem8uDAtwhWJiAQWGxtL69atSUpKIj4+3utyRCRM4uPjSUpKonXr1sTGRn4gSyOZLkjPOMqi9bvL1Wfhul2MG9BB12iKiCdiY2Np0qQJTZo0wVqLtbb0TiIStYwxri9FqpDpgvlrdx13DWZpcvMt89ft0nq+IuI5L345iUjlp9PlLti4s2ynyUvasONAmCsRERERcYdCpguysnNd7SciIiLiNYVMFyQmVOyqhIr2ExEREfGaQqYLurZuWKF+KW0iP72AiIiISCQoZLpg2JmtiIsp30XzcTGGYd1aRagiERERkchSyHRBwzo1GXxGy3L1GaIlJkVERKQSU8h0ycShnUlpU7bT5iltGjJxaHKEKxIRERGJHIVMlyTUiOXR0b24qHvroKfO42IMF3VvzV/H9NLa5SIiIlKp6fZlFyXUiOXO4V0ZN6AD89ftYsOOA2Rl55KYEEdKmySG6RS5iIiIVBEKmR5oUCeB0f3aazUfERERqbJ0ulxEREREwk4hU0RERETCTiFTRERERMJOIVNEREREwk4hU0RERETCTiFTRERERMJOIVNEREREwk4hU0RERETCLiwh0xjTwxjzgDHmHWPMHmOMNcYcLUO/ccaYVcaYDGNMujFmrjGmbyl9+vrapfv6rTLGXBeOr0NEREREwiNcK/78P+CS8nQwxjwN3A1kAQuBmsBgYIgx5ipr7awAfS4DZuCE42XAfuACYIox5gxr7W9P6KsQERERkbAIV8j8HFgPrPZ97AvV2BhzPk7APAD0sdZ+49vfB1gCvGKMWWKtPVikTwPgFSAWuMJa+45vf1PgE+BuY8z71tqPw/Q1iYiIiEgFheV0ubX2CWvtw9baOdbaH8rQ5R7f9lF/wPQ9zufAi0B94PoSfW707X/XHzB9fX4A7vd9qpFMERERkSjg+o0/xpiaOKe4AWYGaOLfN7LE/hEh+nwAHAUG+R5fRERERDzkxd3lnYAE4Cdr7e4Ax9f4tikl9qeUOF7AWnsM2IRzXWfHMNUpIiIiIhUUrmsyy6O1bxsoYGKtzTTGHAIaGGPqWmsPG2PqASeF6ufbf5bv8deXVoQxJjXIoXal9RURERGR0LwYyazj2x4J0SazRNs6RY4F61eyj4iIiIh4xIuRTOPb2jK0CfZ5WfqEZK3tHPBBnBHO5PI8loiIiIgU50XIPOzb1g7RppZvm1Gij//YL2XoE91mtYCsvYWfD1kBjc72rh4p5mBGNvPW7mTjznSysnNJTIgjpU0Sw7q1okGdBK/LExERiXpehMydvm3LQAeNMbVxrr88ZK09DGCt/cUY8zPOFEYtgbQAXf2PtzPAsehTNGACLOztbBv3g8HL3a9HAMjOyWPSglQWrd9Nbn7xwfY12/bz+tItDOnWiolDk4mPi/WoShERkejnxTWZXwPZQGNjTKCg2d233VBi//oSxwsYY2oAXXyP+3WY6vTGT5/Am8b5+OWb0ttL2GTn5PHQ1FXMW7vruIDpl5tvmbtmJw++uYrsnDyXKxQREak8XA+Z1tos4CPfp1cGaOLfN6fE/g9C9BmBM33RYmttqWumR4UxFtrdELrNnA5O2FxRcl56iYRJC1LZsCO9TG037EjnxYWBBtRFREQEvBnJBHjat33IGHOaf6dvWcmbca65fLlEn5d8+y8xxlxepE8T4O8lHrdyOPslJ2yOLGXEctsrhaObR390p7ZqJj3jKIvWB5sdK7CF63ZxMCM7QhWJiIhUbmEJmcaY4caYFf4P3+74ovuMMcP97a21HwLPAUnAOmPMbGPMXGAZUAO43lpbbEjJ9/n1QD4w0xjzsTFmBs7p8fbA89baxeH4elxXt70TNsdYaHJu6LbvNHXC5sY/u1NbNTE/xCnyYHLzLfPX7YpQRSIiIpVbuEYyGwNnF/kAZ0qhovsaF+1grb0LmABsBgYDfYHFwABr7duBnsS3/1xgAdANuAjYihNK7wzT1+KtQUudsDl0Veh2Gx8uHN3MzQzdVkq1cWfZTpOXtGHHgTBXIiIiUjWE5e5ya+0UYIob/ay1nwIXlve5Kp2knk7YtBZmt4Cs74O3ne6bf/7s/0G7Ce7UV8VkZee62k9ERKSq8+qaTCkrY+CyvU7gHFDyXqgSVl5fOLqZr/BTHokJFft7q6L9REREqjqFzMqkxXAnbI46VnrbaTWcsLl3XuTrqgK6tm5YoX4pbZLCXImIiEjVoJBZGcXUKLxRqNfk0G2XXOSEzVktnVPvEtCwM1sRF1OulUmJizEM69YqQhWJiIhUbgqZlV37Xzth86pAK20WkbUHpsY4gfPAF+7UVok0rFOTwWcEXIQqqCFaYlJERCQohcyqokbdwtHNLv8vdNsFPZ2wuXiQO7VVEhOHdialTdlOm6e0acjEockRrkhERKTyUsisilL+7ITNy0LckQ7ww+LCG4UytrlTWxRLqBHLo6N7cVH31kFPncfFGC7q3pq/jumltctFRERC0K2xVVniyU7YBPjsV7D99eBt32vnbNvfDL1ejHxtUSqhRix3Du/KuAEdmL9uFxt2HCArO5fEhDhS2iQxTKfIRUREysRY3QxSjDEmNTk5OTk1NdXrUiLjly0wp2PZ2l7+E9RsFNl6REREJGp17tyZtLS0NGtt5/L21eny6qZeh8JrNxv1Cd32ncbOqfTUx9ypTURERKoMhczqbMhnTtgc/FnodusfLLKE5RF3ahMREZFKTSFToHEfJ2yOzoeEUiYXn17bCZvf/Z87tYmIiEilpJAphYyBK/Y7gfPc2aHbfj6uyBKWea6UJyIiIpWHQqYE1vISJ2xek11622lxTtj8flHk6xIREZFKQSFTQouNL7xR6Kx/hW778RAnbL7XXktYioiIVHMKmVJ2HX7jW8Ly59DtMrYWLmF5cJ0rpYmIiEh0UciU8qtRr3B0M/n3odvOO9MJm0uGu1ObiIiIRAWFTDkx3R5zwuale0K32zu38EahzB3u1CYiIiKeUciU8KjVvHB0s/U1odu+29YJm1/c4UppIiIi4j6FTAm/ftOcsDk8LXS7Lf8sHN3MTnenNhEREXGFQqZETv3TC0c3G3QP3fbtJCdsbv6HO7WJiIhIRClkijsu/NIJm4OWhW639r7C0c2cDHdqExERkbBTyBR3NenvW8IyD+Lqhm47o64TNtc/5E5tIiIiEjYKmeINEwNX/+IEzn4zQrdN/WuRJSxz3alPRERETohCpniv9ZW+JSyPlt52Wg0nbG59JfJ1iYiISIUpZEr0iE0ovFGo7djQbVdeXzi6qSUsRUREoo5CpkSnvq87YfOyfaW39S9huW9x5OsSERGRMonzugCRkBKbOmET4IOu8POm4G0/GlT47zEa3RQREfGSQqZUHsM3Otuf0+CDzqHbvmmc7UWb4KRS2oqIiEjYKWRK5VM/uXCk0h8mg5nbxdenMwwPMQoqIiIiYaVrMqVy898odH4p12P+nFp4o1BWGa7zFBERkROikClVw8nn+yZ5zy+97axmTtj8dEzk6xIREammFDKlajGmcHSzdylzae6YWji6mZvpTn0iIiLVhEKmVF2njnfC5qic0ttOr+OEzY1/jnhZIiIi1YFu/JGocTAjm3lrd7JxZzpZ2bkkJsSR0iaJYd1a0aBOQsUfOCau8Eah9Q85y1QGs/Fh5wNgVC7ExFb8eUVERKoxY7VaSjHGmNTk5OTk1NRUr0upNrJz8pi0IJVF63eTm3/892NcjGFIt1ZMHJpMfFyYQl9OBsyoW7a2Z70AHW4Nz/OKiIhUIp07dyYtLS3NWlvu+QB1ulw8lZ2Tx0NTVzFv7a6AARMgN98yd81OHnxzFdk5eeF54hp1Cq/dbHVF6LZf3FZ47aaIiIiUiUKmeGrSglQ27EgvU9sNO9J5cWFa+IvoP9MJm5fuLr2tP2zunRf+OkRERKoQhUzxTHrGURatL0OwK2Lhul0czMiOTEG1WhSObtZpH7rtkos0uikiIhKCQqZ4Zn6IU+TB5OZb5q/bFaGKirj4GydsDllZelt/2Dy4IfJ1iYiIVBIKmeKZjTvLdpq8pA07DoS5khAa9Soc3SzNvDOcsPl2o8jXJSIiEuUUMsUzWdm5rvY7Yf6w2W9G6HbZB4osYfm9O7WJiIhEGYVM8UxiQsWmaa1ov7BpfWU5lrBs7oTNj4ZGvi4REZEoopApnunaumGF+qW0SQpzJRVUdAnLbo+HbrtvYZElLI+4U5+IiIiHFDLFM8PObEVcTPnuzo6LMQzr1ipCFZ2A5N85YfOaMtz5Pr22EzbX3hf5ukRERDyikCmeaVinJoPPaFmuPkNOdInJSIuNLxzdPGVc6Lab/1E4umnLcOpdRESkElHIFE9NHNqZlDZlO22e0qYhE4cmR7iiMOrzqhM2ryzDXfRTY52w+e1/I1+XiIiICxQyxVMJNWJ5dHQvLureOuip87gYw0XdW/PXMb3Ct3a5m+IbFI5u1u8Suu2qmzTJu4iIVAke36Yr4gTNO4d3ZdyADsxft4sNOw6QlZ1LYkIcKW2SGBbtp8jLY/hGZ5uxDd5rF7qtP2ievwhOHhTZukRERMJMIVOiRoM6CYzu157R/UpZ0rEqqHNq4QTvpY1afjS48N9lmRReREQkCuh0uYjX/KfSB39aetuCJSzXRbwsERGRE6GRTJFo0bhv2Uc3551Z+G+NboqISBTSSKZINPKPbvZ9s/S2WsJSRESikEKmSDRrO7r8S1jO7xn5ukREREqhkClSGRRdwrLjnaHbpn+hJSxFRMRzCpkilU2PZ31LWGaV3ta/hOWqiREvS0REpCiFTJHKKrZm4ehmk3NDt/32RS1hKSIirlLIFKkKBi11wublP5Te1r+E5dcvRL4uERGpthQyRaqSmk0KRzdjaoRu++XtWsJSREQiRiFTpKoadcwJmxdtKr2tP2zuXRD5ukREpFrQZOwiVd1Jncs+yfuSYYX/1iTvIiJyAjSSKVKd+E+lD5xXelv/6Oah1MjXJSIiVY5GMkWqo+bDyj66ObeLs42Jh1HZka1LRESqDI1kilR3/tHNHv8M3S7/WOHo5tEf3alNREQqLYVMEXF0vM23hGVe6W3faeqEzQ8HRL4uERGplBQyRaQ4E1M4utn+5tBtf1xWOLqZd9Sd+kREpFLwNGQaY3obY941xuw3xhw1xmwxxjxqjKkVos84Y8wqY0yGMSbdGDPXGNPXzbpFqo1eLzph8+rM0tu+leiEzS/vjnxdIiIS9TwLmcaYscAnwMXAdmAuUBN4EPjMGFM3QJ+ngVeBLsCHwCpgMLDMGHOZO5WLVENxtQpHNxv2DN3262eLLGGpaZBERKorT0KmMaYl8BIQC1xvrT3LWns5cBowAzgD+HuJPucDdwMHgDOstZdaa4cB5wJ5wCvGmAYufhki1dOwVU7YvGxv6W2nxjhh89uXIl+XiIhEFa9GMsfjjFousta+4t9prc0GbgWOADcYY5KK9LnHt33UWvtNkT6fAy8C9YHrI1y3iPglNisc3SzNql9rCUsRkWrGq5DZw7ddUvKAtfYnIA2oAVwEYIypCVzgazIzwOP5940Ma5UiUjb+sHnh2tLb+sPmvo8iX5eIiHjGq5BZ27c9GOR4um97hm/bCUgAfrLW7g7Qfo1vmxKe8kSkQhp0K/vo5kcXaHRTRKQK82rFn5982zZBjvv3t/VtW/u2gQIm1tpMY8whoIExpq619nBpBRhjgq2V1660viJSBv6gufs9WHZJ6Lb+oDniK6jXMbJ1iYiIK7wayVzq2442xsQXPWCM6Q34f8v47zCv49seCfGY/jlW6oRoIyJua3lx2Uc353RyAufMhpGvS0REIsqrkPkGsBNnhPJdY0xnY0xdY8wwnLvLc33t8n1b//m0UL+lynXOzVrbOdAHsLU8jyMi5eAPm93+HrrdsYOFp9KzD7hTm4iIhJUnIdNamwmMwAmaw4BNwC/APJxg+bSvqf+aTf/p79oE55/APSOsxYpI+CXf54TNUbmlt327kRM2P74o8nWJiEjYeDYZu7V2I84NPdcB/wQmAbfgTLTu579ucqdv2zLQYxljagMnAYfKcj2miESJmNjC0c1Trgvd9vt5RZawPOZOfSIiUmFe3fgDgLU2C3jN91HAGDPI988lvu3XQDbQ2BjTMsAd5t192w0RKlVEIq3PFOcjJwNmHLfgV3FvJTjb5N9Dt8ciXZmIiFSAp2uXB2KMGYATGlOttZ9CQRj1T6p3ZYBu/n1zIl+hiERUjTqFo5v1k0O3TfublrAUEYlSXq5d3s0YE1diX3fgTZwbfG4v0cV/neZDxpjTivTpA9yMc03ny5GrWERcNzzVCZuX7Cy9rX8Jy+8XRr4uEREplZeny58Fko0x64D9OHNino1z48/N1tqPiza21n5ojHkOuBNYZ4xZBMQDg3HC8lhrbToiUvXUblU4BVJpk7d/PLTw32WZNklERCLCy5D5OnAt0A3npp2fgGnAk9badYE6WGvv8oXS23DCZQ6wGGc9808iXrGIeM8fHA+shgW9Qrf1B9IL10MDLQgmIuImz0KmtfYl4KUK9JsCTAl3PSJSyST1LPvo5jzfCrV1O8DIryNbl4iIAB7fXS4ilc/BjGzmrd3Jxp3pZGXnkpgQR0qbJIZ1a0WDOgneFOUPmz8shcUDg7c7vKUwkF66B2o1j3hpIiLVlUKmiJRJdk4ekxaksmj9bnLzi1/ruGbbfl5fuoUh3VoxcWgy8XGx3hTZdEDZRzdnt3C2ra6E/jMiW5eISDUUdVMYiUj0yc7J46Gpq5i3dtdxAdMvN98yd81OHnxzFdk5eS5XGIB/GqQ+/xe63a6ZhdMg5WjBMBGRcFHIFJFSTVqQyoYdZZu8YcOOdF5cmBbhisrhlGvLvoTljLpO2NzwcOTrEhGp4hQyRSSk9IyjLFpfcpGt0Bau28XBjOwIVVRBRZew7PpI6Lab/lw4upkfBaOyIiKVkEKmiIQ0P8Qp8mBy8y3z1+2KUEVh0PVhJ2xeXYbT49PinLC5bUrEyxIRqUoUMkUkpI07K7bGwYYdB8JcSQTE1S4c3WwzKnTbFRMKRzdFRKRUCpkiElJWdhmuZQxjP8+cM9UJm5d9X3pbf9jc91Hk6xIRqaQ0hZGIhJSYULG3iYr2K4+IzNmZeHLhNEgfdIafQ9zE9NEFhbVcfDT65g8VEfGQQqaIhNS1dUPWbNtf7n4pbZIiUI3DtTk7h6c620ObYG7XkE0bvFeTMcCNuyaxK6dV+GsREalkdLpcREIadmYr4mLKdx1iXIxhWLdWEanHkzk7T+pSeO1mKV5qPZEF7UZwR6MXIlOLiEgloZApIiE1rFOTwWe0LFefIRE8Rez5nJ3+sHn+opDNhtefz4J2I1jQbgS1YjKjb/5QEZEIU8gUkVJNHNqZlDYNy9Q2pU1DJg5Njkgd0TRnZ3qdfgz/7gOGbn2/1LazTrmGBe1GkLDtX9E3f6iISIQoZIpIqRJqxPLo6F5c1L110FPncTGGi7q35q9jekXs2sNomrOzsBbD0K1zGLp1Dk/9eEfIPrck/YcG79V07kzPU9gUkapNN/6ISJkk1IjlzuFdGTegA/PX7WLDjgOu30V9InN2ju7XPuK1LDw8hIWHhxBDHnNOvYxYkx/8Ad6q6Wz7zYTWV4S1NhGRaKCQKSLl0qBOAqP7tQ97aCuLaJqzM9Rj5hPLRdveA+C8Okt4oOk/gj/QJ1c627jacNUvYHSCSUSqBoVMkUogIvNBVsJaomnOzrI+5scZA/k4YyDxJpv3Tw0xYpmbCVN9lxkMWg5N+oWhShGpSqLpd0FZKGSKRDHX5oOsJLVE05yd5a3lmE1g6NY5jD+vI6MbzoJ19wdv/GF/Z9vwLBi2+gQrFZHKLhrefytC52VEopQn80FGeS3RNGfnCdWSfJ8zDdIVpazvnv5F4RKWP28+gWpFpLKKlvffilDIFIlSns8HGYW1RNOcnWGpJaFh4bybp/0mdOcPkp2w+emYClQrIpVVtLz/VoRCpkgUiqr5IKOoFoieOTvDXkvPfzlh8+LvQj/QjqmFo5tZ35ejWhGpbKLt/be8FDJFolB0zgfpfS0QPXN2RqyWOm0LRzebXRi67azmTthc94fyFy8iUS/a3n/LSzf+iEShaJ8P0qta/KJhzk5XajlvrrNNXwvzuwdvl/Y35wPgqp+hRr2KPZ+IRJVofP8tD4VMkShUWeaDjES/8vByzk5Xa2l4pjOyaS28fxpkbA3edkZ9Z9tzEpx2S/hrERHXRPP7b1koZIpEoco4H2S4+lVWrsxfZwxc/K3z7+8XwsdDg7ddPdH5ALgmG2Ljw1ODiLimsr//RkcVIlJMZZ4PMpK1RCPP5q9rNsQZ3czPhWk1Qrd9yxdyz30XWl4cvhpEJKIq+/uvbvwRiUJVZj7IKi4q5q+LiSu8Uaj3lNBtl13i3Cj0diPn1LuIRLXK/v6rkCkSharcfJBVVNTNX3fqdU7YvDozdLvsAzA1xgmc+1dEtiYRqbDK/v6rkCkSparsfJBVRFTPXxdXq3B084zHSimqjxM2F54T+bpEpNwq8/uvQqZIlKry80FWcpVm/rrOv3fC5uU/hW63/7PCSd5/2eJObSJSqsr8/musrsspxhiTmpycnJyamup1KSIFDmZkez4fZDTW4qXfv7GyQhfkdz+1EX8be3YEKiqHVTfDt5NLb3fKddBnSsTLEZGy8eL9t3PnzqSlpaVZazuXt69CZgkKmSJSFnf971M27zlU7n6ntziJZ6+PklPTh7fC+2Wc1/OyfZDYNLL1iEjUOZGQqdPlIiIVUNnnrwOgbrvCazebnh+67ayTnVPpGx5xozIRqQIUMkVEKqBr67JdiF9StMxfd5wLFjthc+jq0O02/anw2s2cDHdqE5FKSSFTRKQCKvv8dUElneWEzdH5UKuUWmfUdcLm1pfdqU1EKhWFTBGRCqjs89eVyhi4dKcTOAfODd125Y2Fo5v5Oe7UJyJRTyFTRKSCKvP8deXS/EInbI4qQ4CcFu+EzT2lBFMRqfIUMkVEKqgyz19XIUWXsDy7lFPkS4c7YXNWCy1hKVJNaQqjEjSFkYhURLWdPzQnw7k2syyGrnau+RSRSuNEpjCKork0REQqrwZ1Ehjdrz2j+5Vx3smqokYdZ2QTYOOfYOMjwdsu6OlsmwyEQR9HujIR8ZhOl4uISHh0fdgJnJftC93uxyWFNwod3upKaSLiPoVMEREJr8SmhddunnJd6Lbvt3fC5spfu1ObiLhGIVNERCKnzxQnbI74OnS7rS8Vjm4e/cmV0kQkshQyRUQk8up1KBzdbNQ3dNt3mjhhc9Nf3alNRCJCIVNERNw15FMnbA75PHS7DQ8Vjm7mHnGnNhEJG4VMERHxRqPehUtY1mwSuu302k7Y3PaqO7WJyAlTyBQREW8ZA5f/4ATOc98N3XbF+CJLWOa6Up6IVIxCpoiIRI+WFzth85rs0ttOq+GEze8XRr4uESk3hUwREYk+sfGFNwr1fDF024+HOmHz3VO0hKVIFFHIFBGR6HbazU7YvOrn0O0yt8PUGCdwpq9xpTQRCU4hU0REKoca9QpHNzv/IXTb+T2csPnxMHdqE5HjKGSKiEjlc8ZffUtY7g3d7vsFhTcKZWx3pTQRcShkiohI5ZXYrHB0s83o0G3fO8UJm6tvdac2kWpOIVNERKqGc950wubwzaHbffNvTfIu4gKFTBERqVrqdyoc3Wx4Vui2/knef/rUndpEqhGFTBERqbqGrXbC5uBPQrdb1M8Jm1/cDvk57tQmUsUpZIqISNXX+BzfEpZ5UKN+8HZbXoBp8TC9Hhzc4F59IlWQQqaIiFQfJgauOuS7djMNEhoHbpd7GOad4YxubngEbL6LRYpUDQqZIiJSPdU/Ha74EUblQKd7grfb9CeYGgvvngoZ37lXn0glp5ApIiLVW0wcdP+HM7o5ZAVgArfL/A7eO9UZ3fz6BS1hKVIKhUwRERG/RmfDmHy4JgtOuS54uy9vd5awnN8Lsn5wrz6RSkQhU0REpKTYmtBnijO6ed6C4O3SV8Osk53Rze1TXStPpDJQyBQREQml2RAnbF71MzQfEbzdZ2N866VfCMd+dq8+kSilkCkiIlIWNerBwPedwHnOW8HbfT8fZp7kBM69810rTyTaKGSKiIiUV5urnbB5+Q+Q1Dt4uyUXOmHzs19BbpZ79YlEAc9DpjGmtzHmbWPMPmNMjjEm3Riz2BhzZYg+44wxq4wxGb72c40xfd2sW0REhJpNYOjnTuDsOSl4u+2vw/RaviUsP3evPhEPeRoyjTFXAZ8ClwO7gLeBTcBAYIYx5vEAfZ4GXgW6AB8Cq4DBwDJjzGXuVC4iIlLCabc4YfPi76DuacHbLerrhM0v74L8XNfKE3GbZyHTGBMH/MtXwyhrbU9r7Shr7blAP+AocL8xpl2RPucDdwMHgDOstZdaa4cB5wJ5wCvGmAZufy0iIiIF6rSFkVtgdD6k/CV4u6+fg2k1YGZDOJTqWnkibvFyJLMT0Bj4ylpb7Apqa+3nwAKcGXF7FDnkX5LhUWvtNyXavwjUB66PZNEiIiJlYgx0ecgZ3bxoI8QHGQM5dhDmdnFGNzc9qknepcrwMmRml7FdOoAxpiZwgW/fzADt/PtGnmBdIiIi4XVSF7gy3VnCsuNdwdtt+H/OJO/vnQaZO1wrTyQSvAyZ23wfnYwxVxc9YIzpAwwFvgOW+XZ3AhKAn6y1uwM83hrfNiUy5YqIiJygmDjo8Ywzujn4s+DtMr6Fd9s6o5vfvOhaeSLh5FnItNbmAeOBn4G3jDGrjTHTjDFLgU+AdcAQa+0xX5fWvm2ggIm1NhM4BDQwxtQt7fmNMamBPoB2pfUVERE5YY37OGHz6iPQ9trg7VZPdMLmwr5w9Cf36hM5QZ7eXW6tXQ4MwBmxPAu4BucmnkycO8f3Fmlex7c9EuIhM0u0FRERiW5xidD3/5zAOXBe8Hb7P4d3mjiBc+cM9+oTqSCvpzAaDawEdgJn44TDDsBU4CHgQ2NMDX9z3zbUFdEmxLFirLWdA30AW8v7dYiIiIRF82FO2LzyEDS7MHi7T652wuaSkZDzi2vliZSHl1MYnYYz3+VPwHBr7Sprbaa19htr7c3A+0AfYIKvy2HftnaIh63l22ZEomYRERFXxNeH8+Y6gbPvm8Hb7Z0DM+o7gXPfh+7VJ1IGXo5kjgJqAPN911OWNN23Hejb7vRtWwZ6MGNMbeAk4JC19nCgNiIiIpVO29FO2Lzse2jYM3i7jwY7YXPFBMgr6wQuIpHjZcj0h8Vg4/z+/Q19269xpj1qbIwJFDS7+7YbwlOeiIhIFEk8GYatciZ57/HP4O22TYG3asKbMbB/lWvliZTkZcjc59ueFeS4/8+17QDW2izgI9++QOua+/fNCUdxIiIiUckY6HibbwnLrVD7lCANLSw82xndXHMv5Oe5WqaIlyHzXd/2XGPMxKIHjDG9cZaPhOITrz/t2z7ku6bT374PcDPO6OfLkSlXREQkytQ5FS7ZBqPzoOsjwdt99RRMi4O3m8DPm10rT6o3L+fJXAP8w/fpv40xm4wx040xnwCf4tzgM9la+2GRPh8CzwFJwDpjzGxjzFycCdtrANdba9Nd/UJERES8ZmKg68O+JSw3QI16gdtl/wQfJDujm6mPawlLiSiv58m8D7gcWAicDFwGJANLgbG+u8xL9rkL547zzcBgoC+wGBhgrX3bncpFRESi1Eld4aqfYdQx6HB78Hbrf+8sYTknGTJ3uVefVBvG6q+YYowxqcnJycmpqalelyIiIhIeP30Ki/qV3q7Xf6H9jZGvRyqNzp07k5aWluabS7xc4iJRkIiIiN/BjGzmrd3Jxp3pZGXnkpgQR0qbJIZ1a0WDOglel1c9ND7HOZWeewRW3gg7pgZut+rXzkfjftB/FtRs5G6dUqVoJLMEjWSKiIRHdk4ekxaksmj9bnLzj/9dExdjGNKtFROHJhMfF+tBhdXcng9g6YjS2/WbCa2viHw9EpVOZCTT02syRUSkasrOyeOhqauYt3ZXwIAJkJtvmbtmJw++uYrsHE2v47oWw31LWKbDyYODt/vkSudGoWWXQo7WOpGyU8gUEZGwm7QglQ07yjbZx4Yd6by4MC3CFUlQ8Q3g/IVO4OzzWvB2u9+FGfV8S1h+FLydiI9CpoiIhFV6xlEWrd9drj4L1+3iYIaWQvTcKb9ywuale6DBmcHbfXSBEzZX3qglLCUohUwREQmr+SFOkQeTm2+Zv07T6ESNWs3hwjXOEpbdnwnebuvLzhKW0+Ih/Uv36pNKQSFTRETCauPOiq2JsWHHgTBXIifMGOh0lzO6OfIbqNUqcLv8HJh/ljO6ue4BLWEpgEKmiIiEWVZ2rqv9xCV128OlO50lLDs/FLxd2hPOEpbvNINftrhXn0QdzZMpIiJhlZhQsV8tFe1XHpqzMwxMDJzxF+fj4DpYeA7kHTm+3dF9MKej8+9uf4fT73VGRqXaUMgUEZGw6tq6IWu27S93v5Q2SRGoxhFqzs412/bz+tItmrOzIhp0g2syIe8YfHknfPti4Hbr7nc+6neB8+ZDrRauline0OlyEREJq2FntiIupnwjVnExhmHdglzvd4I0Z6cLYuOh1yTn2s1BS4O3+3kTzG7pXLu59X/u1SeeUMgUEZGwalinJoPPaFmuPkMieLpac3a6rMm5Tti8OgNaXxW83cobnLD54UDI1k1fVZFCpoiIhN3EoZ1JadOwTG1T2jRk4tDkiNShOTs9FFcb+k13Aue5s4O3+3EpvN3ICZy733WtPIk8hUwREQm7hBqxPDq6Fxd1bx301HlcjOGi7q3565heEbsOUnN2RomWlzhh84oD0PS84O2WXeqEzeVXQm6ma+VJZOjGHxERiYiEGrHcObwr4wZ0YP66XWzYccD1O7pPZM7O0f3ah7kaIaEhXOBbknLrK7Dy+sDtdr3tfABcsASaDnCjOgkzhUwREYmoBnUSGN2vvSehTXN2RrF2E5yPI3tgyUVwaEPgdosHOtv2N0OP552bjKRS0OlyERGpsqJ5zk7xqdUCLlrvLGF55j+Ct/v2P/BWAryVCOlr3atPKkwhU0REqqyurct281FJkZyzU4IwBk6/x7l2c8TXkNg8cLu8ozC/u3Pt5voHwea7W6eUmUKmiIhUWdE2Z6eUUb0OcNkeGJULyb8P3i71MZgaC7Nawi/fuFeflIlCpoiIVFnRNmenlFNMLHR7zBndHPYFxAT5f8naA3M6OKObm58GW74ZBSQyFDJFRKRKi5Y5O+UENewBo47CNUeh3a+Dt1t7D0yNgbnd4Mhe18qT4ylkiohIlRYtc3ZKmMQmwNmTndFN/3RIgRxaD7NbOKOb215zrz4pYKyGlIsxxqQmJycnp6amel2KiIiE2cGMbM/m7JQIysmAz8fB7lmh2zW9APrPgPgG7tRVBXTu3Jm0tLQ0a23n8vZVyCxBIVNERKQS2/UOLL+i9HYD3ocWIyJfTyV3IiFTp8tFRESk6mh1uW8Jy/3Q5Nzg7ZaOdE6lf3IN5B5xr75qRCFTREREqp6EJBi01AmcZ78UvN3O6TC9thM4f1zuXn3VgEKmiIiIVG3tbnDC5iU7oX6Is74fnuuEzdW/gfwc9+qrohQyRUREpHqo3QqGb3KWsOz2RPB230yCafEwvQ4cXO9efVWMQqaIiIhUL8ZA8v3O6ObwzVCzaeB2uZkwr5szurnhj1rCspwUMkVERKT6qt8JLt/nLGF5+n3B2236i7OE5btt4fBW18qrzBQyRURERGJi4cy/O6ObQ1eBCTIpf+YOeL+9M7r59fNawjIEhUwRERGRopJ6wuhcZwnLU68P3u7LO50lLOf1gKx97tVXSShkioiIiAQSmwC9X3ZGN8//MHi7g2tgVjNndPO7192rL8opZIqIiIiU5uQLnLB51S/Q4uLg7T7/lRM2PxoKxw65Vl40UsgUERERKasadWHAu07g7DcjeLt9C2FmAydw7pnrXn1RRCFTREREpCJaX+mEzct/hMbnBG+3dLgTNj8dC7lZ7tXnMYVMERERkRNRszEM/sQJnD1fDN5ux5swvZYTOH/6zL36PKKQKSIiIhIup93sW8JyO9TrGLzdonOcsPnFnZCf61p5blLIFBEREQm32m1gxFfOEpZnPBa83ZbnYVoNmHESHNrkWnluUMgUERERiRRjoPPvfUtYpkJCUuB2OT/D3K7O6ObGP1eJJSwVMkVERETcUD8ZrtgPo3Kg02+Dt9v4sLOE5XvtIWO7a+WFm0KmiIiIiJti4qD7U87o5pAVwdtlbIX3TnFGN7f8u9ItYamQKSIiIuKVRmc7YfOaLDjluuDtvrjVWcJywdlw9Ef36jsBCpkiIiIiXoutCX2mOIHzvAXB2x1YBe80dUY3ozxsKmSKiIiIRJNmQ3xLWP4MzYcHb/dOU/dqqgCFTBEREZFoVKMeDJzjBM5zpnldTbkpZIqIiIhEuzbXOGHzsn1w8hCo3xmuTPe6qpDivC5ARERERMoosSmcH+KazSiikUwRERERCTuFTBEREREJO4VMEREREQk7hUwRERERCTuFTBEREREJO4VMEREREQk7hUwRERERCTuFTBEREREJO4VMEREREQk7hUwRERERCTuFTBEREREJO2Ot9bqGqGKM+SUhIaFuu3btvC5FRERExFNbt24lOzv7sLW2Xnn7KmSWYIzZB9QCdrnwdP4ku9WF56pM9LoEp9cmML0uwem1CUyvS2B6XYKrrq9NK+CItfbk8nZUyPSQMSYVwFrb2etaoolel+D02gSm1yU4vTaB6XUJTK9LcHptyk/XZIqIiIhI2ClkioiIiEjYKWSKiIiISNgpZIqIiIhI2ClkioiIiEjY6e5yEREREQk7jWSKiIiISNgpZIqIiIhI2ClkioiIiEjYKWSKiIiISNgpZIqIiIhI2ClkioiIiEjYKWSKiIiISNgpZLrIGFPLGHOpMeZlY8wGY8wvxphMY8x6Y8wfjTF1vK4xWhhjGhpjfjTGWGPMV17X4zVjzMnGmGeMMVuMMVnGmHRjzJfGmL97XZuXjDG9jTFvG2P2GWNyfK/LYmPMlV7XFmnGmB7GmAeMMe8YY/b4flaOlqHfOGPMKmNMhu/1mmuM6etGzW4oz+tijIkxxvQ3xvzdGLPS956TbYzZaox50Rhzitv1R1JFv2dKPMaHvn7WGHNypGp10wn8LCUYY+41xnzh+32eYYz52vc7voUbtUc7TcbuImPMjcB/fZ+mAmlAPaAvUBf4Chhgrf3RmwqjhzFmCjAOMMDX1tpO3lbkHWNMH2AucBLO98wmnO+XZKCltTbOu+q8Y4y5CpiG88fyF8BWoDlwjm/fE9baB7yrMLKMMbOBS0rszrbW1gzR52ngbiALWAjUBC7A+Tm7ylo7KzLVuqc8r4sxpj3wje/TPTjfR/lAL6AFcBi4yFr7ScQKdlFFvmdK9B8PvAJYnO+ZZtbafeGs0QsV/FlqAnwIdAX2AZ/7DrX37etfVb5vToRCpouMMeOA3sAz1tpviuxvBnwAnAlMtdaO8ajEqGCMuQDnh3cycBPVOGQaY5rj/EGSAIwtGQKMMb2stas8Kc5Dxpg4YC/QGBhlrX2ryLE+wEc4r9lp1tqt3lQZWcaY3wG1gNW+j32E+MVojDkfWAwcAPr434N8r9cSnOB5irX2YOSrj5zyvC7GmHbAv4DHrLXLiuxPAF4ExgM7gfbW2pzIVx9Z5f2eKdG3MbAZ+BLoCLSh6oTM8v4sxQCf4vw+/yvwiLU2t8jxU4FfrLX7I117tFPIjBK+N/rPgGygnrX2mMclecIYkwhsAI4BlwJbqN4h8zXgV8Dt1toXvK4nWhhjugAbga+stacHOD4bZ2TiGmvtdJfL84QxxhL6F+MHwEXA3dbaZ0scew64A7jXWvtUpGt1U2mvS4h+NXHCRn1goLV2aSTq81J5XhtjzBvA5UAXnD9WqkzILKkMP0vXAy8Db1trq/ylOSdC12RGj/W+bQKQ5GUhHnsYaAdMBCr9yMGJMMY0AK4GfgZe8ricaJNdxnbpEa2ikvAFpgt8n84M0MS/b6Q7FUU/a+1RnD9ywbkMo9oyxgwFxgB/rapnBsrpZt+2Sv1BFgnV8lquKHWqb5tDNf3FaIxJAe4BXrHWLjPGtPW4JK+dg/NHx4dAju9mln5ADZzrd6dba3/wsD4vbfN9dDLGXF10tNJ3VmAo8B2wLEj/6qYTzvfST9ba3QGOr/FtU9wrKboZY2JxRuvAGdGslowxtXAuHfgKqNY3GgIYY+oCZ+Fcr7vS935zMdAQ59KKd621mzwsMaooZEaPO33b+dbaso7SVBm+a1z+CxwC7ve2mqjR2bf9AVgO9Clx/G/GmAnW2hnuluU9a22e7yaE94G3jDH34dz40wwniK8CflVdLzsJoLVvGyhgYq3NNMYcAhoYY+paaw+7Vln0GgU0AX7CuZSpuvoL0BY4Tz9PgHPDZQzwLfA8cGuJ438xxvzDWqvfY+h0eVQwxlwE3IAzivn/PC7HK7fj3NF5n7X2gNfFRIkGvu04nBGmG3BudDkFeBqoDbzuGwGudqy1y4EBOCOWZwHXAOcCmTijv3u9qy7q+KdHOxKiTWaJttWWMaYV8Kzv0z9Wxz/8AYwx3XEGQF611i7xuJxo4X9f7ooTMP+B857cGPg1zg109xljbvGmvOiikOkxY8zpwOs400HcZ61dX0qXKsf3hv4osNRaO8XjcqJJrG8bB/zWWvs/a+1+a+12a+09ONfRxVNNR36NMaOBlTinqM7GCUcdgKnAQ8CHxpga3lUYVYxvG+pOTxPiWLVhjKkNzAIaAbOttS96XJInfJcL+M8u3ettNVGl6PvyVGvtfb735P3W2pcofD9+0JvyootCpoeMMS2B+Th/GT1trX3O45K88m+csDTR60KijP+UZT7waoDj//NtB7pSTRQxxpyG85r8BAy31q6y1mZaa7+x1t6Mcxq9DzDByzqjiP97qXaINrV824wI1xK1fH+UvA30AD7BudmluroL6A7cr6l4iil6Kcn/Ahz3zyPa0jcPa7WmazI9YoxpBCzCuVbqFar3X4ojcP5anmRMscEU//QRrY0xS/xtrbXV5Zfgdt92X5DTdf7jTVypJrqMwrkBar61NjPA8ek4d0oPxJlvtbrb6du2DHTQN3p3EnCoul6P6bsu/HWcm8bWAyOttVneVuWpkThh6TrfHM9F+Vf6eccYcwx4qBpNPL69yL93lDxorT1ijPkJ5325Cc61m9WWQqYHfHenzcO54/Md4NdWE5aehHN9XSCJRY5Vp+/Ztb5tA2OMCfA94p/qqrqE7qL8YemXIMf9+xu6UEtl8DXOtE+NjTEtA9xh3t233eBuWVHl3zhThm0BhlhrD3lbTlQwONc5B+O/GbGRC7VEBWvtTmPMAZz33+PeX3x/rJzk+7Q6vjcXo9PlLvOtJPEuzo0KC4DR1to8b6vylrXWBPrAuZganMnY/fsPeViqq6y1G3FuaknEueawpIG+7ZoAx6o6/5QyZwU53tO33R75UqKfb0TuI9+ngSaP9u+b405F0cUY8xjO3Ic7gcFa2hestQNDvDf7R/Ca+fbN9rBUL7zv254X4FhfnMu/snCmfarWFDJd5LuQeirON+Zy4HJNCSGleMK3fd53iQUAxpgeOHOKgjOHXXXzrm97rjGm2LW8xpjeOOtzQ+CJx6urp33bh3zXtAIF84rejDP6+7IXhXnJGPNb4Pc4f7gMstbuLKWLyJNAHs5d5Gf6d/rWM/ffW/E//X7XspKuMsbcSeG0GLMIfqrvXl1oDb7J2L+jei8rGQNMA67CmaT/M5y7qP1/Lf/XWnuTdxV6xxjzJIXXMqcCaTgrs/TB+QN6su8moCrJGDOc4lOenY1zDV3Rtez/Yq39oEifZ3GmpDmCc014PDAY5/W62lr7doTLjrjyvC7GmG44ZwIM8DmFK/yU9FJVuOawIt8zQR5nO1VoWckK/izdjjNPZjbO904GzgIaDXC+pwZW1+ubi6pO17dFgwZF/n1ZiHaPANU+ZApYa/ONMaOAJcCNwPk4b35fAC9aa//Pw/I8Za29zxjzGXALzt3AHXHu/FyKEwre9LI+FzTm+MsoTIl9jYsetNbeZYxZB9yGEy5zcNahfrQqhCif8rwuJ1E4dVMfjl/wwG8Jzt3mlV25v2eqiYr8LP3TGPM1zh+6vXBuVN2KM5D0D2ttqDlpqw2NZIqIiIhI2OmaTBEREREJO4VMEREREQk7hUwRERERCTuFTBEREREJO4VMEREREQk7hUwRERERCTuFTBEREREJO4VMEREREQk7hUwRERERCTuFTBEREREJO4VMEREREQk7hUwRERERCTuFTBEREREJO4VMEREREQk7hUwRERERCTuFTBEREREJO4VMEREREQk7hUwRERERCbv/D8WmkyFfo6AmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])\n",
    "y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])\n",
    "slope, intercept, r, p, std_err = stats.linregress(x, y)\n",
    "mymodel = slope * x + intercept\n",
    "\n",
    "plt.figure(dpi=150, figsize=(5, 3)) # 设置分辨率和尺寸大小\n",
    "plt.scatter(x, y, color=\"steelblue\", label=\"origin data\")\n",
    "plt.plot(x, mymodel, color=\"orange\", label=\"linear regression\")\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 评价模型\n",
    "**拟合优度：**<br/>\n",
    "使用$R^2$来度量拟合优度，$R^2$越大，拟合相关性越好。<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.758591524376155\n"
     ]
    }
   ],
   "source": [
    "print(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])\n",
    "y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])\n",
    "y_pred = slope * x + intercept\n",
    "\n",
    "\n",
    "plt.figure(dpi=150, figsize=(5, 3)) # 设置分辨率和尺寸大小\n",
    "plt.scatter(x, y, label=\"origin data\") # 加上label才能显示图例\n",
    "plt.scatter(x, y_pred, label=\"prediction\")\n",
    "plt.legend(loc = \"upper right\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 多项式回归\n",
    "[返回引言](#top)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array([1,2,3,5,6,7,8,9,10,12,13,14,15,16,18,19,21,22])\n",
    "y = np.array([100,90,80,60,60,55,60,65,70,70,75,76,78,79,90,99,99,100])\n",
    "mymodel = np.poly1d(np.polyfit(x, y, 3)) # 3次多项式拟合\n",
    "myline = np.linspace(1, 22, 100)\n",
    "\n",
    "plt.figure(dpi=150, figsize=(5, 3)) # 设置分辨率和尺寸大小\n",
    "plt.scatter(x, y, label=\"origin data\")\n",
    "plt.plot(myline, mymodel(myline), color=\"orange\", label=\"polynomial line\")\n",
    "plt.legend(loc=\"upper center\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 评价模型\n",
    "**拟合优度：**<br/>\n",
    "使用$R^2$来度量拟合优度，$R^2$越大，拟合相关性越好。<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9432150416451026\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "print(r2_score(y, mymodel(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=150, figsize=(5, 3)) # 设置分辨率和尺寸大小\n",
    "plt.scatter(x, y, label=\"origin data\")\n",
    "plt.scatter(x, mymodel(x), color=\"orange\", label=\"prediction\")\n",
    "plt.legend(loc=\"upper center\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 多元线性回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 糖尿病数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>0.041708</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.019662</td>\n",
       "      <td>0.059744</td>\n",
       "      <td>-0.005697</td>\n",
       "      <td>-0.002566</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.031193</td>\n",
       "      <td>0.007207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>-0.005515</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.015906</td>\n",
       "      <td>-0.067642</td>\n",
       "      <td>0.049341</td>\n",
       "      <td>0.079165</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>-0.018118</td>\n",
       "      <td>0.044485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>0.041708</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.015906</td>\n",
       "      <td>0.017282</td>\n",
       "      <td>-0.037344</td>\n",
       "      <td>-0.013840</td>\n",
       "      <td>-0.024993</td>\n",
       "      <td>-0.011080</td>\n",
       "      <td>-0.046879</td>\n",
       "      <td>0.015491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>-0.045472</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.039062</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>0.016318</td>\n",
       "      <td>0.015283</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>0.026560</td>\n",
       "      <td>0.044528</td>\n",
       "      <td>-0.025930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>-0.045472</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.073030</td>\n",
       "      <td>-0.081414</td>\n",
       "      <td>0.083740</td>\n",
       "      <td>0.027809</td>\n",
       "      <td>0.173816</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.004220</td>\n",
       "      <td>0.003064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6  \\\n",
       "437  0.041708  0.050680  0.019662  0.059744 -0.005697 -0.002566 -0.028674   \n",
       "438 -0.005515  0.050680 -0.015906 -0.067642  0.049341  0.079165 -0.028674   \n",
       "439  0.041708  0.050680 -0.015906  0.017282 -0.037344 -0.013840 -0.024993   \n",
       "440 -0.045472 -0.044642  0.039062  0.001215  0.016318  0.015283 -0.028674   \n",
       "441 -0.045472 -0.044642 -0.073030 -0.081414  0.083740  0.027809  0.173816   \n",
       "\n",
       "            7         8         9  \n",
       "437 -0.002592  0.031193  0.007207  \n",
       "438  0.034309 -0.018118  0.044485  \n",
       "439 -0.011080 -0.046879  0.015491  \n",
       "440  0.026560  0.044528 -0.025930  \n",
       "441 -0.039493 -0.004220  0.003064  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "437    178.0\n",
       "438    104.0\n",
       "439    132.0\n",
       "440    220.0\n",
       "441     57.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)\n",
    "df_X = pd.DataFrame(diabetes_X)\n",
    "df_y = pd.Series(diabetes_y)\n",
    "\n",
    "df_X.tail()\n",
    "df_y.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-3.634285e-16</td>\n",
       "      <td>1.308343e-16</td>\n",
       "      <td>-8.045349e-16</td>\n",
       "      <td>1.281655e-16</td>\n",
       "      <td>-8.835316e-17</td>\n",
       "      <td>1.327024e-16</td>\n",
       "      <td>-4.574646e-16</td>\n",
       "      <td>3.777301e-16</td>\n",
       "      <td>-3.830854e-16</td>\n",
       "      <td>-3.412882e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.072256e-01</td>\n",
       "      <td>-4.464164e-02</td>\n",
       "      <td>-9.027530e-02</td>\n",
       "      <td>-1.123996e-01</td>\n",
       "      <td>-1.267807e-01</td>\n",
       "      <td>-1.156131e-01</td>\n",
       "      <td>-1.023071e-01</td>\n",
       "      <td>-7.639450e-02</td>\n",
       "      <td>-1.260974e-01</td>\n",
       "      <td>-1.377672e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-3.729927e-02</td>\n",
       "      <td>-4.464164e-02</td>\n",
       "      <td>-3.422907e-02</td>\n",
       "      <td>-3.665645e-02</td>\n",
       "      <td>-3.424784e-02</td>\n",
       "      <td>-3.035840e-02</td>\n",
       "      <td>-3.511716e-02</td>\n",
       "      <td>-3.949338e-02</td>\n",
       "      <td>-3.324879e-02</td>\n",
       "      <td>-3.317903e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.383060e-03</td>\n",
       "      <td>-4.464164e-02</td>\n",
       "      <td>-7.283766e-03</td>\n",
       "      <td>-5.670611e-03</td>\n",
       "      <td>-4.320866e-03</td>\n",
       "      <td>-3.819065e-03</td>\n",
       "      <td>-6.584468e-03</td>\n",
       "      <td>-2.592262e-03</td>\n",
       "      <td>-1.947634e-03</td>\n",
       "      <td>-1.077698e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.807591e-02</td>\n",
       "      <td>5.068012e-02</td>\n",
       "      <td>3.124802e-02</td>\n",
       "      <td>3.564384e-02</td>\n",
       "      <td>2.835801e-02</td>\n",
       "      <td>2.984439e-02</td>\n",
       "      <td>2.931150e-02</td>\n",
       "      <td>3.430886e-02</td>\n",
       "      <td>3.243323e-02</td>\n",
       "      <td>2.791705e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.107267e-01</td>\n",
       "      <td>5.068012e-02</td>\n",
       "      <td>1.705552e-01</td>\n",
       "      <td>1.320442e-01</td>\n",
       "      <td>1.539137e-01</td>\n",
       "      <td>1.987880e-01</td>\n",
       "      <td>1.811791e-01</td>\n",
       "      <td>1.852344e-01</td>\n",
       "      <td>1.335990e-01</td>\n",
       "      <td>1.356118e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0             1             2             3             4  \\\n",
       "count  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02   \n",
       "mean  -3.634285e-16  1.308343e-16 -8.045349e-16  1.281655e-16 -8.835316e-17   \n",
       "std    4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02   \n",
       "min   -1.072256e-01 -4.464164e-02 -9.027530e-02 -1.123996e-01 -1.267807e-01   \n",
       "25%   -3.729927e-02 -4.464164e-02 -3.422907e-02 -3.665645e-02 -3.424784e-02   \n",
       "50%    5.383060e-03 -4.464164e-02 -7.283766e-03 -5.670611e-03 -4.320866e-03   \n",
       "75%    3.807591e-02  5.068012e-02  3.124802e-02  3.564384e-02  2.835801e-02   \n",
       "max    1.107267e-01  5.068012e-02  1.705552e-01  1.320442e-01  1.539137e-01   \n",
       "\n",
       "                  5             6             7             8             9  \n",
       "count  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  \n",
       "mean   1.327024e-16 -4.574646e-16  3.777301e-16 -3.830854e-16 -3.412882e-16  \n",
       "std    4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  \n",
       "min   -1.156131e-01 -1.023071e-01 -7.639450e-02 -1.260974e-01 -1.377672e-01  \n",
       "25%   -3.035840e-02 -3.511716e-02 -3.949338e-02 -3.324879e-02 -3.317903e-02  \n",
       "50%   -3.819065e-03 -6.584468e-03 -2.592262e-03 -1.947634e-03 -1.077698e-03  \n",
       "75%    2.984439e-02  2.931150e-02  3.430886e-02  3.243323e-02  2.791705e-02  \n",
       "max    1.987880e-01  1.811791e-01  1.852344e-01  1.335990e-01  1.356118e-01  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "count    442.000000\n",
       "mean     152.133484\n",
       "std       77.093005\n",
       "min       25.000000\n",
       "25%       87.000000\n",
       "50%      140.500000\n",
       "75%      211.500000\n",
       "max      346.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0    0.217952\n",
       "1    0.095322\n",
       "2    0.260831\n",
       "3    0.244444\n",
       "4    0.280694\n",
       "5    0.314401\n",
       "6    0.283486\n",
       "7    0.261629\n",
       "8    0.259696\n",
       "9    0.273379\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_X.describe()\n",
    "df_y.describe()\n",
    "df_X.apply(lambda x: x.max() - x.min(), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "自变量均值都为0，浮动范围小，看起来数据已经**标准化**了。<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**线性关系检验:**<br/>\n",
    "通过散点图检验自变量与因变量之间的线性关系。<br/>\n",
    "```\n",
    "plt.figure(dpi=150, figsize=(10, 20)) # 设置分辨率和尺寸大小\n",
    "\n",
    "for i in range(df_X.columns.size):\n",
    "    plt.subplot(5, 2, i+1)\n",
    "    plt.scatter(df_X.iloc[:, i], df_y)\n",
    "    plt.title(\"第{}个变量\".format(i), \n",
    "              fontproperties=font_manager.FontProperties(fname=\"C:/Windows/Fonts/msyhl.ttc\")) # 雅黑细体\n",
    "\n",
    "plt.show();\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**密度图:**<br/>\n",
    "上图数据太大，点太多，需要用密度图才能更好的观察分布。<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x3000 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=150, figsize=(10, 20)) # 设置分辨率和尺寸大小\n",
    "\n",
    "for i in range(df_X.columns.size):\n",
    "    plt.subplot(5, 2, i+1)\n",
    "    plt.hexbin(df_X.iloc[:, i], df_y)\n",
    "    plt.colorbar(label=\"count\") # 添加图例\n",
    "    plt.xlim(df_X.iloc[:, i].min(), df_X.iloc[:, i].max()) # 坐标范围\n",
    "    plt.ylim(df_y.min(), df_y.max())\n",
    "    plt.title(\"第{}个变量\".format(i), \n",
    "              fontproperties=font_manager.FontProperties(fname=\"C:/Windows/Fonts/msyhl.ttc\")) # 雅黑细体\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可用看出，自变量与预测变量之间的线性关系还算明显，除了第1个变量(0开始)。<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据预处理\n",
    "因为数据本身就归一化了，所以这里只**划分训练集与测试集**。<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>-0.038207</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.071397</td>\n",
       "      <td>-0.057314</td>\n",
       "      <td>0.153914</td>\n",
       "      <td>0.155887</td>\n",
       "      <td>0.000779</td>\n",
       "      <td>0.071948</td>\n",
       "      <td>0.050276</td>\n",
       "      <td>0.069338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.001751</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.005128</td>\n",
       "      <td>-0.012556</td>\n",
       "      <td>-0.015328</td>\n",
       "      <td>-0.013840</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.006080</td>\n",
       "      <td>-0.067351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>0.023546</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>0.062039</td>\n",
       "      <td>0.024574</td>\n",
       "      <td>-0.036073</td>\n",
       "      <td>-0.091262</td>\n",
       "      <td>0.155345</td>\n",
       "      <td>0.133396</td>\n",
       "      <td>0.081764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>0.048974</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.060618</td>\n",
       "      <td>-0.022885</td>\n",
       "      <td>-0.023584</td>\n",
       "      <td>-0.072712</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.104138</td>\n",
       "      <td>0.036201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>0.034443</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.038540</td>\n",
       "      <td>-0.012556</td>\n",
       "      <td>0.009439</td>\n",
       "      <td>0.005262</td>\n",
       "      <td>-0.006584</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.031193</td>\n",
       "      <td>0.098333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6  \\\n",
       "230 -0.038207  0.050680  0.071397 -0.057314  0.153914  0.155887  0.000779   \n",
       "98   0.001751  0.050680 -0.005128 -0.012556 -0.015328 -0.013840  0.008142   \n",
       "322  0.023546  0.050680  0.061696  0.062039  0.024574 -0.036073 -0.091262   \n",
       "382  0.048974 -0.044642  0.060618 -0.022885 -0.023584 -0.072712 -0.043401   \n",
       "365  0.034443 -0.044642 -0.038540 -0.012556  0.009439  0.005262 -0.006584   \n",
       "\n",
       "            7         8         9  \n",
       "230  0.071948  0.050276  0.069338  \n",
       "98  -0.039493 -0.006080 -0.067351  \n",
       "322  0.155345  0.133396  0.081764  \n",
       "382 -0.002592  0.104138  0.036201  \n",
       "365 -0.002592  0.031193  0.098333  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.045472</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.047163</td>\n",
       "      <td>-0.015999</td>\n",
       "      <td>-0.040096</td>\n",
       "      <td>-0.024800</td>\n",
       "      <td>0.000779</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.062913</td>\n",
       "      <td>-0.038357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>0.034443</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.018584</td>\n",
       "      <td>0.056301</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>-0.054549</td>\n",
       "      <td>-0.069172</td>\n",
       "      <td>0.071210</td>\n",
       "      <td>0.130081</td>\n",
       "      <td>0.007207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0.056239</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.057941</td>\n",
       "      <td>-0.007966</td>\n",
       "      <td>0.052093</td>\n",
       "      <td>0.049103</td>\n",
       "      <td>0.056003</td>\n",
       "      <td>-0.021412</td>\n",
       "      <td>-0.028320</td>\n",
       "      <td>0.044485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>0.110727</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.033151</td>\n",
       "      <td>-0.022885</td>\n",
       "      <td>-0.004321</td>\n",
       "      <td>0.020293</td>\n",
       "      <td>-0.061809</td>\n",
       "      <td>0.071210</td>\n",
       "      <td>0.015567</td>\n",
       "      <td>0.044485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>0.048974</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.041774</td>\n",
       "      <td>0.104501</td>\n",
       "      <td>0.035582</td>\n",
       "      <td>-0.025739</td>\n",
       "      <td>0.177497</td>\n",
       "      <td>-0.076395</td>\n",
       "      <td>-0.012908</td>\n",
       "      <td>0.015491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6  \\\n",
       "6   -0.045472  0.050680 -0.047163 -0.015999 -0.040096 -0.024800  0.000779   \n",
       "394  0.034443 -0.044642  0.018584  0.056301  0.012191 -0.054549 -0.069172   \n",
       "200  0.056239 -0.044642 -0.057941 -0.007966  0.052093  0.049103  0.056003   \n",
       "402  0.110727  0.050680 -0.033151 -0.022885 -0.004321  0.020293 -0.061809   \n",
       "261  0.048974 -0.044642 -0.041774  0.104501  0.035582 -0.025739  0.177497   \n",
       "\n",
       "            7         8         9  \n",
       "6   -0.039493 -0.062913 -0.038357  \n",
       "394  0.071210  0.130081  0.007207  \n",
       "200 -0.021412 -0.028320  0.044485  \n",
       "402  0.071210  0.015567  0.044485  \n",
       "261 -0.076395 -0.012908  0.015491  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "230    220.0\n",
       "98      92.0\n",
       "322    242.0\n",
       "382    132.0\n",
       "365    206.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "6      138.0\n",
       "394    273.0\n",
       "200    158.0\n",
       "402    168.0\n",
       "261    103.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_X, test_X, train_y, test_y = train_test_split(df_X, df_y, test_size=0.3, random_state=123)\n",
    "\n",
    "train_X.tail()\n",
    "test_X.tail()\n",
    "\n",
    "train_y.tail()\n",
    "test_y.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "regr = linear_model.LinearRegression() \n",
    "regr.fit(train_X, train_y) # 训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([160.55133583, 155.90772541, 228.86013518, 168.36334509,\n",
       "       156.16182462, 133.79324508])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_y = regr.predict(test_X)\n",
    "pred_y[:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 评价模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "自变量系数：\n",
      " [  10.45384922 -261.16601105  538.84541221  280.72544466 -855.21447839\n",
      "  472.17305267  166.51881384  309.88763264  684.0489522   102.37723262]\n",
      "截距: 152.61\n",
      "RMSE: 2926.80\n",
      "Rsquared: 0.51\n",
      "MAE: 0.40\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_percentage_error \n",
    "\n",
    "print(\"自变量系数：\\n\", regr.coef_) \n",
    "print(\"截距: %.2f\" % regr.intercept_)\n",
    "print(\"RMSE: %.2f\" % mean_squared_error(test_y, pred_y))\n",
    "print(\"Rsquared: %.2f\" % r2_score(test_y, pred_y))\n",
    "print(\"MAE: %.2f\" % mean_absolute_percentage_error(test_y, pred_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型$R^2$才0.49，表现差强人意。<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 逻辑回归\n",
    "[返回引言](#top)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 乳腺癌数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   V1  V2  V3  V4  V5    V6  V7  V8  V9   class\n",
       "0   5   1   1   1   2   1.0   3   1   1  benign\n",
       "1   5   4   4   5   7  10.0   3   2   1  benign\n",
       "2   3   1   1   1   2   2.0   3   1   1  benign\n",
       "3   6   8   8   1   3   4.0   3   7   1  benign\n",
       "4   4   1   1   3   2   1.0   3   1   1  benign"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>malignant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>malignant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>malignant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     V1  V2  V3  V4  V5   V6  V7  V8  V9      class\n",
       "694   3   1   1   1   3  2.0   1   1   1     benign\n",
       "695   2   1   1   1   2  1.0   1   1   1     benign\n",
       "696   5  10  10   3   7  3.0   8  10   2  malignant\n",
       "697   4   8   6   4   3  4.0  10   6   1  malignant\n",
       "698   4   8   8   5   4  5.0  10   4   1  malignant"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(\"F:/py_input_output/data_input/biopsy.csv\").drop(\"ID\", axis=1)\n",
    "df1.head()\n",
    "df1.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**平衡性检验:**<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "benign       458\n",
       "malignant    241\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"class\"].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isna().all(axis=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**缺失值检验:**<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "V1        0\n",
       "V2        0\n",
       "V3        0\n",
       "V4        0\n",
       "V5        0\n",
       "V6       16\n",
       "V7        0\n",
       "V8        0\n",
       "V9        0\n",
       "class     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "V1       0.00000\n",
       "V2       0.00000\n",
       "V3       0.00000\n",
       "V4       0.00000\n",
       "V5       0.00000\n",
       "V6       0.02289\n",
       "V7       0.00000\n",
       "V8       0.00000\n",
       "V9       0.00000\n",
       "class    0.00000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.agg(lambda x: np.sum(pd.isna(x))) # 各列缺失值数量\n",
    "df1.agg(lambda x: np.sum(pd.isna(x))/len(x)) # 各列缺失值比例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出变量`V6`存在缺失值。<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**相关性检验:**<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IhG4AABiZ6SUAAEyr5l4EZMsy0g0AACMTugEAYGSmlwAAMC3TSwAAgPUSugEAYGRCNwAAjMycbgAAJlU7Fn8cePE/IQAATEzoBgCAkZleAgDAtCwZCAAArJfQDQAAIxO6AQBgZOZ0AwAwqarFHwde/E8IAAATW3forqoXVdWDNqIYAABYRHNPL6mqo9banOSiJG+pqk8mSXffPce5dibZuXLbPffcM28pAACwpQwZ6f77NdrHMgvu1yf5+PK2eexKctfKtrS0NKAUAADYOoaE7tuTvCXJNyf5puX2uCSfTvL0JGcsb5vHUpKjV7Zdu3YNKAUAgIVRtb62BQxZveRfJLk8yQuSPLW7/zpJqqqT/M/u/pN5T9Tde5LsGVIoAABsVXOPdHf3x7r73yb5tST/s6qePF5ZAACwOAav093dv1BVv5fkDVX17SPUBADAdmKd7ntV1cn7fl6eSvINST6S5KYkuze8MgAAWBBDRrpvrKr3JXlNkjd0911Jnj1OWQAAsDiGjOWfluTGJJckub2qXl9VZ4xTFgAALI4hN1Je393PSHJMkvOTHJfk7VV1S1VdVFXHjVUkAACLq3bUutpWMHjWenfv7u4ru/v0JCcluTrJM5PcWlVv3uD6AABgy1vXraLdfUtm000uTnJ3ksdvRFEAALBIBi8ZuE9VPTbJuUnOTrI3yTWZPTwHAADmt0WeKrkeg0J3VR2f5JzldmKS65I8K8k13f2JjS4OAAAWwdyhu6reluSMJB9NclWSK7r75rEKAwCARTFkpHt3ZlNJ3tTde0eqBwAAFs7cobu7nzhmIQAAbFMeAw8AAKyX0A0AACM75CUDAQBgI9Q2WDLQSDcAAIxM6AYAgJEJ3QAAMDKhGwAARiZ0AwAwrR071tcGqqoLqurWqrqnqm6oqscc4NjXVlWv0T4w6CMOrhIAALaoqnpSklcmuTjJKUneleQtVXXCft7yw0mOXdGOT/KxJL825LpCNwAAW1pV7ayqo1a1nfs5/NlJLu/u13T3B7v7wiQfTnL+Wgd3913d/ZF9Lckjkjwgya8MqVHoBgBgUlW1rpZkV5K7VrVda1znfklOTXLtql3XJnnUnOU+Pcnbu/u2IZ/Rw3EAANjqlpJcumrbnjWOe1CSI5LcsWr7HUmOOdhFqurYJN+a5HuGFih0AwCwpXX3nqwdsvf7llWva41tazknyceT/MaAayUxvQQAgO3jziR7c99R7QfnvqPfn6Fm81jOTfK67v7k0AsL3QAATKt2rK/NaTks35DkzFW7zkxy3UHe/tgkX57k8iEfbR/TSwAA2E4uTfK6qnpvkuuTnJfkhCSvTpKqWkrykO5+2qr3PT3JH3T3TYdyUaEbAIBto7vfWFUPTPLCzNbdvinJWStWIzk2sxD+z6rq6CRnZ7Zm9yGp7nnmjG+Kw6YQAIAFVVMXsJb3XXTeunLgKRf/0mH5uVYy0g0AwLTqsM/M63ZYhe4/ffUlU5cwua/6/uclSd574ZMnrmR6j3jl1UmSG37snGkLOQyc+rLXJklu+qnnTVvIYeBrnuP3BABbj9VLAABgZIfVSDcAANtPDVj2b6ta/E8IAAATE7oBAGBkQjcAAIxM6AYAgJG5kRIAgGntWPx1uo10AwDAyIRuAAAYmeklAABMaxs8Bt5INwAAjEzoBgCAkQndAAAwMnO6AQCYVNXijwMv/icEAICJCd0AADAy00sAAJiWJQMBAID1EroBAGBkQjcAAIzMnG4AACZlyUAAAGDdhG4AABiZ0A0AACMzpxsAgGntsE43AACwTkI3AACMTOgGAICRmdMNAMC0rNN9cFX1hVX1WRtRDAAALKK5Q3dVnVdVO5d/rqp6flX9fZKPJPl4VV1acz5OqKp2VtVRK9uePXsO7RMAAMBhbshI9y8kOXr55/OSPD/JTyR5TJLnJjk3yQVznmtXkrtWtqWlpQGlAADA1jEkdK9cQPHpSV7Q3Zd293Xd/XNJfjTJM+Y811JmAf6f265duwaUAgDAoqiqdbWtYOiNlL3854lJ3rFq3zuT/PRcJ+nek8R8EgAAtoWhofsJVXVXkt1J7r9q3/2TfHpDqgIAgAUyNHRfueLnxyX5gxWvvzHJLeuuCACA7WWLTBFZjyGh++u7+/0H2P+RzG6QBAAAVhhyI+WNVfXeqjq/qo5avbO739Tdb93A2gAAYCEMCd2nJXlfkkuSfKSqXl9VZ4xTFgAALI65Q3d3X9/dz0hyTJLzkxyX5O1VdUtVXVRVx41VJAAAC2zHjvW1LWBwld29u7uv7O7Tk5yU5Ookz0xya1W9eYPrAwCADVVVF1TVrVV1T1XdUFWPOcjxO6vq4qq6rar2LA86nzvkmkNXL/kM3X1LVV2S5MNJXprk8es5HwAAjKmqnpTklZk9Sf09mQ0ev6WqHtbdf7mft12T5Isye0Dk/07y4AzM0YccuqvqsZk9+v3sJHuXi7n8UM8HAMD2tMlPlXx2ksu7+zXLry+sqsdnNn36PivxVdUTkjw2yZd198eWN39o6EUHTS+pquOr6gVVdUuS303y0CTPSvLF3f2M7v79oQUAAMB6LE//OGpV27nGcfdLcmqSa1ftujbJo/Zz+icmeW+SH6uqv66qP6uql1fV6gdFHtDcI91V9bYkZyT5aJKrklzR3TcPuRgAAIxgV5IXrdr2kiQvXrXtQUmOSHLHqu13ZLZYyFq+LMmjk9yT5N8un+OyJF+Q2ayPuQyZXrI7s6kkb+ruvQPeBwAAY1pKcumqbXsOcHyvel1rbNtnx/K+p3T3XUlSVc9O8t+q6ge6e/c8Bc4durv7ifMeCwAAm6W79+TAIXufOzO7F3H1qPaDc9/R731uT/LX+wL3sg9mFtSPS/Ln89S4NRY2BABgcdWO9bU5dfcnk9yQ5MxVu85Mct1+3vaeJF9cVZ+7YttJST6d5K/mvbbQDQDAdnJpku+rqnOr6qur6qeTnJDk1UlSVUtVddWK49+Q5O+S/EpVPayq/nWSn8rs/sa5ppYk61ynGwAAtpLufmNVPTDJC5Mcm+SmJGd1923LhxybWQjfd/w/VtWZSV6V2Somf5fZUtk/PuS6QjcAANtKd1+W2Qoka+07Z41tf5r7TkkZROgGAGBSm/xwnEmY0w0AACMTugEAYGSmlwAAMK0By/5tVYv/CQEAYGJCNwAAjEzoBgCAkZnTDQDAtHZYMhAAAFgnoRsAAEZmegkAAJPyREoAAGDdhG4AABhZdffUNexz2BQCALCgDst5HH/yMy9aVw582A+/5LD8XCuZ0w0AwLS2wWPgD6vQfdPLnz91CZP7mh99aZLkfRedN3El0zvl4l9Kktz43O+duJLpff1P/kqS5P0vumDiSqZ38ksuS5J84BUXTVzJ9B7+IxdPXQIAc1r8/6wAAICJCd0AADAyoRsAAEZ2WM3pBgBgG/JwHAAAYL2EbgAAGJnpJQAATKp2LP448OJ/QgAAmJjQDQAAIxO6AQBgZOZ0AwAwLUsGAgAA6yV0AwDAyEwvAQBgUlWLPw68+J8QAAAmJnQDAMDIhG4AABiZOd0AAEzLkoEAAMB6Cd0AADAyoRsAAEZmTjcAANOyTjcAALBeQjcAAIxM6AYAgJGZ0w0AwKRqh3W6AQBgoVTVBVV1a1XdU1U3VNVjDnDs6VXVa7SvGnJNoRsAgG2jqp6U5JVJLk5ySpJ3JXlLVZ1wkLd+ZZJjV7Q/H3JdoRsAgGlVra8N8+wkl3f3a7r7g919YZIPJzn/IO/72+7+yIq2d8hFDzl0V9WRVXVmVT29qr65qo441HMBAMChqqqdVXXUqrZzjePul+TUJNeu2nVtkkcd5DLvq6rbq+odVXXG0BrnDt1V9bNV9W3LPx+X5I+TvCWzofnfWS7kIXOe6z4ds2fPnqG1AwBAkuxKcteqtmuN4x6U5Igkd6zafkeSY/Zz7tuTnJfk7CT/LsnNSd5RVf96SIFDRrq/M8lfLP/8iiR/leSY7j4myYOT3JbZ/Jh53KdjlpaWBpQCAAD/bCnJ0avagcJlr3pda2ybHdh9c3f/cnff2N3Xd/cFSX47yY8OKXBI6H5AknuWf35Ukou6+87lYj6WWZA+fc5z3adjdu1a6z9GAABYeLVjXa2793T33avaWtMo7kyyN/cd1X5w7jv6fSC/n+QrhnzEIaH7z5J8w/LP/5DkqFX7P2/e863VMTt33mfaDQAAbJju/mSSG5KcuWrXmUmuG3CqUzKbdjK3IQ/H+ekkL6+qOzIbqf7ZqnpWkg9mtoTKzyT59SEXBwCATXZpktdV1XuTXJ/ZfO0Tkrw6SapqKclDuvtpy68vTPKhJB9Icr8k/yGz+d1nD7no3KG7u19bVV+Q2RyWymwS+so7P/+/JP9pyMUBAGAzdfcbq+qBSV6Y2XrbNyU5q7tvWz7k2MxC+D73S/LyJA9Jsjuz8P1t3f3mIdedO3RX1cndfWlVXZHkW5KcmNl0ktuTvKe7By0QDgAASVLD19pel+6+LMll+9l3zqrXL0vysvVec8j0khur6n1JXpPkDd1913ovDgAA28GQGylPS3JjkkuS3F5VrzuUhcEBAGC7mTt0L69L+IzMllg5P8nxSd5eVbdU1UXLD8wBAIBhduxYX9sCBlfZ3bu7+8ruPj3JSUmuTvLMJLdW1aAJ5QAAsB2s6z8NuvuWzKabXJzk7iSP34iiAABgkQy5kfIzVNVjk5yb2RqFe5Nck+TyDaoLAAAWxqDQXVXHJzlnuZ2Y2ZN7npXkmu7+xEYXBwAAi2DIOt1vS3JGko8muSrJFd1981iFAQCwPWz2Ot1TGDLSvTuzqSRv6u69I9UDAAALZ8hj4J84ZiEAALCoDvlGSgAA2BC1NdbaXo/F/4QAADAxoRsAAEYmdAMAwMjM6QYAYFrbYMlAI90AADAyoRsAAEZmegkAAJOqHYs/Drz4nxAAACYmdAMAwMiEbgAAGJk53QAATMtj4AEAgPUSugEAYGRCNwAAjEzoBgCAkbmREgCASVXV1CWMrrp76hr2OWwKAQBYUIdlur3l6l9aVw586JPPOyw/10qmlwAAwMgOq+klf/KzL5m6hMk97IdelCR574VPnriS6T3ilVcnSW740adOXMn0Tn3565Ik73/RBRNXMr2TX3JZkuSPL3nOxJVM72uf91NJkve/4PyJKzk8nPwTvzB1CcCh2nHYD1Svm5FuAAAYmdANAAAjE7oBAGBkh9WcbgAAtqFa/HHgxf+EAAAwMaEbAABGZnoJAACT2g5PpDTSDQAAIxO6AQBgZEI3AACMzJxuAACmZclAAABgvYRuAAC2laq6oKpurap7quqGqnrMnO87rao+VVXvH3pNoRsAgGntqPW1AarqSUlemeTiJKckeVeSt1TVCQd539FJrkryjkP6iIfyJgAA2KKeneTy7n5Nd3+wuy9M8uEk5x/kfb+Y5A1Jrj+UiwrdAABsaVW1s6qOWtV2rnHc/ZKcmuTaVbuuTfKoA5z/e5M8NMlLDrVGoRsAgK1uV5K7VrVdaxz3oCRHJLlj1fY7khyz1omr6iuSXJLkKd39qUMt0JKBAABsdUtJLl21bc8Bju9Vr2uNbamqIzKbUvKi7v6z9RQodAMAMKla5zrd3b0nBw7Z+9yZZG/uO6r94Nx39DtJPi/JI5KcUlU/t7xtR5Kqqk8l+Zbufuc8NZpeAgDAttDdn0xyQ5IzV+06M8l1a7zl7iRfm+TkFe3VSW5e/vkP5r22kW4AALaTS5O8rqrem9lKJOclOSGzMJ2qWkrykO5+Wnd/OslNK99cVX+b5J7uvikDCN0AAGwb3f3GqnpgkhcmOTazUH1Wd9+2fMixmYXwDSV0AwAwrRr2gJv16u7Lkly2n33nHOS9L07y4qHXNKcbAABGJnQDAMDITC8BAGBS610ycCtY/E8IAAATE7oBAGBkQjcAAIxs7jndVfWg7r5zzGIAANiGNnnJwCkMGem+o6reUVXfU1U7R6sIAAAWzJDQXUk+meRXktxeVa+qqpMP5aJVtbOqjlrZ9uzZcyinAgCAw97QOd3/MclDklyc5IwkN1TVDVV1flUdPeA8u5LctbItLS0NLAUAgIWwo9bXtoDBN1J2953d/Yru/pokj07y/iQ/meRvquqqOU+zlOTolW3Xrl1DSwEAgC1hSOju+2zovr67n57k2CQ/lOShc52oe093372y7dxpmjgAAItp6JzuNXX3J7r78u4+bQNqAgCAhTIkdD87s/nXAADAAENC96VJrjuEmyYBAGC/qnasq20FQ6o8LcmNSS7JbMnA11fVGeOUBQAAi2Pu0L180+QzkhyT5PwkxyV5e1XdUlUXVdVxYxUJAABb2aEsGbi7u6/s7tOTnJTk6iTPTHJrVb15g+sDAIAt78j1vLm7b6mqS5J8OMlLkzx+Q6oCAGD7qK3xgJv1OOTQXVWPTXJukrOT7E1yTZLLN6guAABYGINCd1Udn+Sc5XZikuuSPCvJNd39iY0uDgAAFsHcobuq3pbkjCQfTXJVkiu6++axCgMAYJvYIsv+rceQke7dmU0leVN37x2pHgAAWDhzh+7ufuKYhQAAwKJa/LF8AACY2LqWDAQAgPX6km89e+HXDDTSDQAAIxO6AQBgZEI3AACMTOgGAICRCd0AADAyoRsAAEYmdAMAwMiEbgAAGJnQDQAAIxO6AQBgZEI3AACMTOgGAICRCd0AADAyoRsAAEYmdAMAwMiqu6euYZ/DphAAgAVVUxewXRnpBgCAkR05dQErffCyi6cuYXJffcFFSZIbnvO0iSuZ3qk/dVWS5IYfO2faQg4Dp77stUmSP37Zc6ct5DDwtT/2k0mSP3rpj0xcyfT+xfNfkST5wCsumriSw8PDf+TivP9FF0xdxmHh5JdcNnUJwCpGugEAYGRCNwAAjEzoBgCAkQndAAAwMqEbAABGJnQDAMDIhG4AABiZ0A0AACMTugEAYGRCNwAAjEzoBgCAkQndAAAwMqEbAABGJnQDAMDIhG4AABiZ0A0AACMTugEAYGRCNwAAjEzoBgCAkQndAAAwMqEbAABGJnQDAMDIhG4AABiZ0A0AACMTugEAYGRCNwAAjEzoBgCAkQndAAAwMqEbAABGduSQg6vqiCQnJLmtuz9dVTuTfEdm4f13u/uOEWoEAIAtbe7QXVVfl+R3kjw4yU1V9W1J3pLkxCSd5J+q6vHd/YejVAoAAFvUkOklL0vy7iRfl+R3k7w1yQeTPGC5/XaSl85zoqraWVVHrWx79uwZVjkAAGwRQ0L3I5O8sLtvSrIryVcmeXl3/1N3fyrJJUlOmfNcu5LctbItLS0NKAUAALaOIaG7knxq+efVfybJ3gHnW0py9Mq2a9euAaUAAMDWMSR035DkuVX1kMxGqm9N8oMr9j8ryU3znKi793T33Svbzp07B5QCAABbx5DVS56X2Tzuc5N8NMkZSa6oqtuTfDqzed3fvuEVAgDAFjckdH8qyZdkNpf75u7+x6o6PclTktw/ydu6++YNrxAAALa4IaH7xuV2eZI/T5Luvmf5NQAAsB9D5nSfluR9ma1S8pGqen1VnTFOWQAAsDjmDt3dfX13PyPJMUnOT3JckrdX1S1VdVFVHTdWkQAAsJUNGelOknT37u6+srtPT3JSkquTPDPJrVX15g2uDwAAtrzBoXul7r4ls+kmFye5O8njN6IoAABYJENupPwMVfXYzJYPPDuzB+NcEzdVAgDAfQwK3VV1fJJzltuJSa7L7KE413T3Jza6OAAAWARzh+6qeltmD8T5aJKrklxhXW4AADi4ISPduzObSvKm7t47Uj0AALBw5g7d3f3EMQsBAIBFta7VSwAAgIMTugEAYGRCNwAAjEzoBgCAkQndAAAwMqEbAABGJnQDAMDIhG4AABiZ0A0AACMTugEAYGRCNwAAjEzoBgCAkQndAAAwMqEbAABGJnQDAMDIhG4AABiZ0A0AACOr7p66hn0Om0IAABZUTV3AdmWkGwAARnbk1AWsdNubf23qEib3JWd9V5Lkz1/38xNXMr2veOoPJEluufqXJq5keg998nlJkr/4tSsmrmR6X/Zd5yZJ/uy1PzNxJdM76ZwfTpL85e/8+sSVHB5OeMK/y21v+e9Tl3FY+JJvPTu3vPGXpy7jsPDQJz1j6hIgiZFuAAAYndANAAAjE7oBAGBkQjcAAIxM6AYAgJEJ3QAAMDKhGwAARiZ0AwDAyIRuAAAYmdANAAAjE7oBAGBkQjcAAIxM6AYAgJEJ3QAAMDKhGwAARiZ0AwDAyIRuAAAYmdANAAAjE7oBAGBkQjcAAIxM6AYAgJEJ3QAAMDKhGwAARiZ0AwDAyIRuAAAYmdANAAAjE7oBAGBkQjcAAIxM6AYAgJEdOfQNVfX/JDk1ybFJ9ia5NcmN3d0bXBsAACyEuUN3Ve1IckmSH0jy2fs2L//5l1X1rO7+rQ2uDwAAtrwh00temuT/TfI9Sc5K8p4kz0vysCRXJfm1qvqWeU5UVTur6qiVbc+ePQNLBwCArWFI6H5qkmd2929291uTPDnJC5Lc2t0vTHJxkhfPea5dSe5a2ZaWlgaUAgAAW8eQ0P15Sf56xevbM5tm8oDl1/89ydfNea6lJEevbLt27RpQCgAAbB1DQvcfZza6vc93J/nH7v7IinPNNUeku/d0990r286dOweUAgAAW8eQ1UtemOS3q+qJSe5J8qgkz1mx/wlJ3reBtQEAwEIYMtL9d0kemeTtSf4wyVnd/cp9O7v75d39uI0tDwAAtr4hI903ZjaSfXmS/9rdd41TEgAALJYhI92nZRa8l5LcXlWvr6ozxikLAAAWx9yhu7uv7+5nJDkmyflJjkvy9qq6paouqqrjxioSAAC2siEj3UmS7t7d3Vd29+lJTkpydZJnJrm1qt68wfUBAMCWNzh0r9Tdt2T2aPiLk9yd5PEbURQAACySITdSfoaqemySc5OcnWRvkmsyu8kSAABYYVDorqrjk5yz3E5Mcl2SZyW5prs/sdHFAQDAIpg7dFfV25KckeSjSa5KckV33zxWYQAAsCiGjHTvzmwqyZu6e+9I9QAAwMKZO3R39xPHLAQAABbVulYvAQAADk7oBgCAkQndAAAwMqEbAABGJnQDAMDIhG4AABiZ0A0AACMTugEAYGRCNwAAjEzoBgCAkQndAAAwMqEbAABGJnQDAMDIhG4AABiZ0A0AACMTugEAYGRCNwAAjKy6e+oa9jlsCgEAWFA1dQHb1eE00l1Ttqr67Kp6SVV99tS1TN30hf7QF/pCX+gLfbGwfcFEDqeR7klV1VFJ7kpydHffPXU9U9IXn0l/3Etf3Etf3Etf3Etf3Etf3EtfkBxeI90AALCQhG4AABiZ0A0AACMTuu+1J8lLlv/c7vTFZ9If99IX99IX99IX99IX99IX99IXuJESAADGZqQbAABGJnQDAMDIhG4AABiZ0A0AACMTugEAYGTbJnRX1W9V1dv3s+8bq6qr6uur6meq6oaq2lNV79/kMjfFnH3x2Kq6uqo+XFW7q+qDVfXDm13r2Obsi1Or6neq6m+WvxcfrqqfW36s70KZ9+/Jim0PrKq/Wt7++ZtW6CYY8Duj12jfv9n1jmnI96KqzqmqP6qqe6rqI1X1c5tb7bjm7Itn7+d70VX14M2ueSwD/o48sqreUVUfr6q/r6prq+rkTS53VAP64nFVdV1V/UNV3V5VP1lVR252vUxj24TuJJcn+aaq+pI19p2b5P3dfWOSSnJFkjduZnGb7KB9keShST6a5D8keXiSi5MsVdUPblaRm2SevviLJL+Z5IlJTkpyTpJvTvLqzSlxU83792Tl8X+0KZVtviF98b1Jjl3RrtycEjfNXH1RVc/O7HfFJZn93nhckrduXpmbYp7fGb+Qz/w+HJtZP/xed//tJtW5Gebpiz/P7LP/ZZJ/meTRSe5O8taq+qxNqnMzzNMXn0ry5iS/k+SUJP8+s39XLtmkGplad2+LluTIJB9J8qJV2z8ns18AP7hq+4sz+4dk8tqn7osV+38+yTunrv8w6YsfSvLhqeufsj+SnJ/kfyT5piSd5POnrn+Kvlj+7P9m6nqn7oskD0jyf5I8bup6p+6LNd7zhUk+meSpU9c/wffiEct/R45fsf9rl7c9dOrPsMl98dIkf7hq/79JsjvJ5039GbTx27YZ6e7uTyW5Ksk5VVUrdn1Xkvsl+a+TFDaBdfTF0Uk+NnJ5m+pQ+qKqvjjJv0vye5tS5Caatz+q6mFJXpjkaUk+vdl1boaB342fq6o7q+oPq+r7q2qhfrfO2RdnZvZ/Tx+yPB3tr6rqmqo6fvMrHs8h/v58Wmb/QfLfxq9w88zZFzcnuTPJ06vqflV1/yRPT/KBJLdtcsmjmbMvdia5Z9Vbdyf57CSnbkadTGuh/mGYwxVJvjTJ6Su2nZvk17v776coaEKD+qKqvjHJdyf5xc0obpPN1RfLc9z/T5K/zmzk4vs2scbNdMD+qKqdSa5O8pzu/svNL29TzfPdeEFm/7B+c5JfTfKKJM/fvBI3zcH64ssy+zfl+UkuTPKdSb4gyduq6n6bWegmGPpvyblJ3tDdu8cvbdMdsC+6+x+W9/2HzALmPyZ5fJKzloPqIjnY9+KtSR5VVU+uqiOq6iFJfnz5uGM3s1AmMvVQ+2a3JO9J8rrlnx+a2SjdN69x3IuzoNNLDqEvHp7kb5P8+NQ1T9kXSY5J8lVJviOzUZrLpq57iv5IcmmSX11x7OlZwOklQ74bq47/kSR3TV33BN+L5y9/D75lxfFfmGRvksdPXftU34sk37jcL6dOXfNE34v7J/mDzO5zeGSSf5XZiP9NSe4/de2b/b1I8uwkd2U2v/sTSZ63/P347qlr18Zv222kO5nd7HD28soT35vZ/956x7QlTeagfbE8jeCdSX65u//L5pe4aQ7aF939ke7+0+7+zSTPTHJ+VS3q6MSB+uObknxXVX2qqj61YvudVfWSzS91dEN/Z/x+kqOq6os2o7hNdqC+uH35zz/Zd3B3fzSzqQUnbGaRm2Te78X3ZTaAc8NmFrfJDtQX35PZ6O/3dvcfdvfvL287MbMBjEVzwO9Fd1+a5PMz+zvxoMxu0k+SWze3TKawHUP3NZmNvHxPkv+Y5Fe6Z//5uQ0dsC+q6uFJfjfJld190TQlbpqh34t9c/Z2jl3YRA7UH2cn+bokJy+3fdNsHpPZzbaLZuh345TM5m1+fPzSNt2B+uI9y39+5b6Dq+oLMgsWCzN3d4WDfi+q6nMzm5Z3+eaXt6kO1Befk9lo78q+2fd6ETPIQb8XPfM3PZtu9OQkH05y433OxMKp7Zg3q+o1md0Id3SSE3vFvNSq+vIkn5vk+5OckeRJy7v+pLs/udm1jm1/fbEicF+b5EdXvGXv8ujVwjlAX5yV5IuS/GFm8xEfluRlST7e3Y+eqNzRHejvyarjTs/su/KA7v74ZtW3mQ7w3fj2zKYdXZ/ZfNUzMpvT/druXrh17ZOD/v78jSRfnuS8zO57WMpsrvfJ3f1Pm1/tuA72d6Sqnp7k55J8cS/4fUMH+DvyVZktl3dFkldlFrSfl+Tbk3x1d9++5gm3sIP8HXlOZksGfnr5mBdkNrXkNyYolc029fyWKVrunWP31jX2/Y/lfavbl05d92b2RWZz2tfqhw9NXfMEfXFGkusyG7ncneTPMltX9fOnrnmK/ljjuNOzwHO6D/LdeEKS9yX5h8zmZ/5xkh9OcuTUNU/xvUhyVGajun+f5O+S/HpWLBW3aO1gf0eWf2/816nrnLovMlvZ5t3Lv0M/ltl0i381dc0T9cU7V/xb8vtJvnXqerXNa9typBsAADbTIs6nAgCAw4rQDQAAIxO6AQBgZEI3AACMTOgGAICRCd0AADAyoRsAAEYmdAMAwMiEbgAAGJnQDQAAIxO6AQBgZP8X1zt8+A3iMiAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from string import ascii_letters\n",
    "import seaborn as sns\n",
    "\n",
    "corr = df1.loc[:,df1.columns.str.startswith(\"V\")].corr() # 计算相关系数\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "fig, ax=plt.subplots(dpi=100, figsize=(10, 8)) # 调节图片分辨率和尺寸\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5}); # 分号避免显示无关文字"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据预处理 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 缺失值插补"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>694</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>malignant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>malignant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>malignant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      V1    V2    V3   V4   V5   V6    V7    V8   V9      class\n",
       "694  3.0   1.0   1.0  1.0  3.0  2.0   1.0   1.0  1.0     benign\n",
       "695  2.0   1.0   1.0  1.0  2.0  1.0   1.0   1.0  1.0     benign\n",
       "696  5.0  10.0  10.0  3.0  7.0  3.0   8.0  10.0  2.0  malignant\n",
       "697  4.0   8.0   6.0  4.0  3.0  4.0  10.0   6.0  1.0  malignant\n",
       "698  4.0   8.0   8.0  5.0  4.0  5.0  10.0   4.0  1.0  malignant"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import KNNImputer\n",
    "imputer = KNNImputer(n_neighbors=3, weights=\"uniform\")\n",
    "df1.iloc[:, 0:-1] = imputer.fit_transform(df1.iloc[:, 0:-1])\n",
    "df1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "V1       0\n",
       "V2       0\n",
       "V3       0\n",
       "V4       0\n",
       "V5       0\n",
       "V6       0\n",
       "V7       0\n",
       "V8       0\n",
       "V9       0\n",
       "class    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "V1       0.0\n",
       "V2       0.0\n",
       "V3       0.0\n",
       "V4       0.0\n",
       "V5       0.0\n",
       "V6       0.0\n",
       "V7       0.0\n",
       "V8       0.0\n",
       "V9       0.0\n",
       "class    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.apply(lambda x: x.isna().sum(), axis=0) # 各列缺失值数量\n",
    "df1.apply(lambda x: x.isna().sum()/x.size, axis=0) # 各列缺失值比例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 划分训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
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       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
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       "       V1    V2    V3   V4   V5    V6   V7    V8   V9\n",
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       "255   5.0   6.0   6.0  2.0  4.0  10.0  3.0   6.0  1.0\n",
       "226  10.0   5.0   7.0  4.0  4.0  10.0  8.0   9.0  1.0\n",
       "157   2.0   1.0   1.0  1.0  2.0   1.0  3.0   1.0  1.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
       "      V1   V2   V3   V4   V5   V6   V7   V8   V9\n",
       "606  4.0  1.0  1.0  2.0  2.0  1.0  1.0  1.0  1.0\n",
       "110  1.0  3.0  1.0  2.0  2.0  2.0  5.0  3.0  2.0\n",
       "280  3.0  1.0  1.0  1.0  2.0  1.0  3.0  1.0  1.0\n",
       "248  4.0  1.0  1.0  1.0  2.0  1.0  3.0  6.0  1.0\n",
       "88   4.0  1.0  1.0  1.0  2.0  1.0  3.0  1.0  1.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "205    malignant\n",
       "622       benign\n",
       "255    malignant\n",
       "226    malignant\n",
       "157       benign\n",
       "Name: class, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "606    benign\n",
       "110    benign\n",
       "280    benign\n",
       "248    benign\n",
       "88     benign\n",
       "Name: class, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_X, test_X, train_y, test_y = train_test_split(df1.drop(\"class\", axis=1), df1[\"class\"], \n",
    "                                                    test_size=0.3, random_state=125)\n",
    "\n",
    "train_X.tail()\n",
    "test_X.tail()\n",
    "\n",
    "train_y.tail()\n",
    "test_y.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 标准化\n",
    "标准化前必须处理缺失值。<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.48809353, 0.05423261, 0.10846523, 0.32539569, 0.21693046,\n",
       "        0.54232614, 0.3796283 , 0.3796283 , 0.10846523],\n",
       "       [0.20145574, 0.40291148, 0.40291148, 0.36262033, 0.24174689,\n",
       "        0.40291148, 0.28203804, 0.40291148, 0.20145574],\n",
       "       [0.81373347, 0.11624764, 0.23249528, 0.34874292, 0.23249528,\n",
       "        0.11624764, 0.23249528, 0.11624764, 0.11624764],\n",
       "       [0.30831321, 0.36997585, 0.36997585, 0.12332528, 0.24665057,\n",
       "        0.61662642, 0.18498792, 0.36997585, 0.06166264],\n",
       "       [0.47036043, 0.23518022, 0.3292523 , 0.18814417, 0.18814417,\n",
       "        0.47036043, 0.37628835, 0.42332439, 0.04703604]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[0.49937617, 0.39950094, 0.49937617, 0.04993762, 0.14981285,\n",
       "        0.49937617, 0.24968808, 0.04993762, 0.04993762],\n",
       "       [0.73029674, 0.18257419, 0.18257419, 0.36514837, 0.36514837,\n",
       "        0.18257419, 0.18257419, 0.18257419, 0.18257419],\n",
       "       [0.12803688, 0.38411064, 0.12803688, 0.25607376, 0.25607376,\n",
       "        0.25607376, 0.6401844 , 0.38411064, 0.25607376],\n",
       "       [0.56694671, 0.18898224, 0.18898224, 0.18898224, 0.37796447,\n",
       "        0.18898224, 0.56694671, 0.18898224, 0.18898224],\n",
       "       [0.47809144, 0.11952286, 0.11952286, 0.11952286, 0.23904572,\n",
       "        0.11952286, 0.35856858, 0.71713717, 0.11952286]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import Normalizer\n",
    "\n",
    "transformer = Normalizer().fit(train_X)\n",
    "train_X_normalized = transformer.transform(train_X)# 对训练集进行标准化\n",
    "test_X_normalized = transformer.transform(test_X)# 对测试集进行标准化\n",
    "\n",
    "train_X_normalized[-6:-1]\n",
    "test_X_normalized[-6:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "regr = linear_model.LogisticRegression()\n",
    "regr.fit(train_X_normalized, train_y) # 训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['malignant', 'benign', 'malignant', 'malignant', 'benign',\n",
       "       'benign'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_y = regr.predict(test_X)\n",
    "pred_y[:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 评价模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[119,  16],\n",
       "       [  6,  69]], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "      benign       0.95      0.88      0.92       135\n",
      "   malignant       0.81      0.92      0.86        75\n",
      "\n",
      "    accuracy                           0.90       210\n",
      "   macro avg       0.88      0.90      0.89       210\n",
      "weighted avg       0.90      0.90      0.90       210\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report, RocCurveDisplay\n",
    "confusion_matrix(test_y, pred_y)\n",
    "print(classification_report(test_y, pred_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:70: FutureWarning: Pass fpr=[0.         0.11851852 1.        ], tpr=[0.   0.92 1.  ], roc_auc=0.9007407407407407 as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error\n",
      "  warnings.warn(f\"Pass {args_msg} as keyword args. From version \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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sVDtm9oBRuv5pwMWZeVHp9akR8RLgPcDpFfZ/FvBAWe/v/RHxVYqeWjXR45u38eBjW4CixqwkSdJYaNqY2dJyuMcAVw/adDXw7CEOuw5YHBEnRGEv4DXAj4e5zrSImNv/AOY0IHwN0t8re+Ces5g3o6O5wUiSpEmjrkUTImIx8HJgP2Bq+bbMPK3K0+xBUcbrkUHtj1CUAdtJafWxNwJXANMp4v8h8IFhrnM6cEaVMalOy6wvK0mSmqDmZDYiXkSRQN4PHALcBjyZYlnb/6sjhhx8iQpt/dc+jGJM7meAn1JMGvs8xbjZtw9x/rOAc8pezwFW1BGnhuHKX5IkqRnq6Zk9C/hCZn4qIjYCr6ZYTOEyisoC1VoL9LJzL+wCdu6t7Xc6cG1mfr70+paI2Az8OiL+LjNXDz4gM7uArv7XloxqvMxk2YpSWS6TWUmSNIbqGTN7KPCt0vMeYEZmbgI+BfxttSfJzG3ATcDxgzYdTzE2tpKZQN+gtt7SV7PUJlnxxFYe37yNqe1tHLrIIcmSJGns1JPMbgamlZ6vAg4q27ZHjec6B3hHRLwtIg6NiHMpxuFeCBARZ0XEpWX7/wg4MSLeExEHRsRzKIYd3JCZq+p4L2qAm0tDDA7dey7TpriasSRJGjv1DDP4DfAc4A6KKgJfiIjDgRNL26qWmVdExO4UvbqLKMbfnpCZD5Z2WUSR3Pbv/82ImAO8H/gCsA74OTX0CKvxtk/+siSXJEkaW/Uks6cBs0vPzyw9Pwm4j+oXVxiQmRcAFwyx7eQKbV8GvlzrdTR6nPwlSZKapeZkNjP/UPZ8C8XqXZqkunv7uG2Vk78kSVJz1FVntl9EzGbQuNvM3DCiiNRS7nlkI53dfcyZPoUDdp/V7HAkSdIkU/MEsIg4ICJ+XCqJtR54ovRYV/qqSWTZ8qJXdsm+82lrs6CEJEkaW/X0zF5W+vo2inqwFRc40OSwdHnx/5cjXflLkiQ1QT3J7BHAMZl5d6ODUevp75l1vKwkSWqGeurM/g7Yt9GBqPVs6urhnjUbATjSslySJKkJ6umZfQdwYUTsQ1EXtrt8Y2be0ojANP7dtnI9mbD3vOksmDu92eFIkqRJqJ5kdk+KVb++UdaWFMvJJuASUJOE9WUlSVKz1ZPMXgLcDLweJ4BNastWrAOKSgaSJEnNUE8yuz/w8sy8r9HBqLU4+UuSJDVbPRPAfg4c2ehA1FrWbOxk5bqttAUcvo+TvyRJUnPU0zP7I+DciDgcuJWdJ4D9sBGBaXzr75V96oI5zJo2ooXkJEmS6lZPFnJh6eunKmxzAtgksX3yl72ykiSpeWpOZjOznqEJmmD6J385XlaSJDWTialq1teXAz2zVjKQJEnNZDKrmj3w2GY2dPYwvaONg/ea0+xwJEnSJGYyq5otLfXKPmPveXS0+yMkSZKax0xENXPlL0mSNF6YzKpmS1e4WIIkSRofTGZVk66eXu5ctQGAJYvnNzcYSZI06dWVzEbELyLim4PavhURP29IVBq37lq9kW29fTxp1lT2fdKMZocjSZImuXqXbnoAWD2obSXQN6JoNO4N1JddPI+IaG4wkiRp0qsrmc3Mt1Zo+/jIw9F4t/ShdYDjZSVJ0vjgmFnVZKkrf0mSpHGkqp7ZiPhgtSfMzC/VH47Gs/Vbu/nDo5sBONLJX5IkaRyodpjBh6vcLwGT2Qnq1lJJrv2eNJMnzZra5GgkSZKqTGYz84DRDkTjX//kryUOMZAkSeNE3WNmI2JqRBwSEfVWRFCLWerKX5IkaZypOZmNiJkRcTGwBbgd2K/U/qWI+FiD49M4kZkDyeySfec1NxhJkqSSenpmzwKOBP4E6Cxr/xlwUgNi0ji0en0nj27sor0tePreJrOSJGl8qGeIwCuBkzLzNxGRZe13AAc1JCqNO8tKvbJPWziH6R3tzQ1GkiSppJ6e2T2BNRXaZ1FUM9AEZH1ZSZI0HtWTzP4O+POy1/0J7DuB60cckcalZQPjZec3NQ5JkqRy9QwzOB34r4g4rHT8hyLi6cBxwAsaGZzGh96+HKgxazIrSZLGk5p7ZjPzOuA5wEzg98CLgUeA4zLzpsaGp/HgvjWb2Lytl1lT2zloz9nNDkeSJGlAXTViM/NW4C0NjkXjVP8Qg8MXz6O9LZobjCRJUpm6ktmIaAdeBRxKMWb2TuAHmdnTwNg0Tjj5S5IkjVc1J7MR8QzgB8BC4O5S88HAoxHx8lKvrSaQgclfi+c3NQ5JkqTB6qlmcBHFyl+LM/PozDwa2Be4BfhaI4NT83V293LXwxsBWLLf/OYGI0mSNEg9wwyOBI7NzCf6GzLziYj4BEXZLk0gt69aT29fsmDONBbOnd7scCRJknZQT8/s3cBeFdoXAPeNLByNNzc/tA4oxstGOPlLkiSNL1X1zEbE3LKXHwe+FBFnAr8ptT0L+BTwtw2NTk23zPqykiRpHKt2mME6dlyqNoAry9r6u+x+BLQ3JDKNC/2Tv4508pckSRqHqk1mXziqUWhcenzzNh56fAtQ1JiVJEkab6pKZjPzV6MdiMafZaX6sgftOYt5MzqaG4wkSVIFdS2aABARM4H9gKnl7Zl5y0iD0vgwMMTA8bKSJGmcqmfRhD2BbwAvG2IXx8xOEEv7F0swmZUkSeNUPaW5zgN2o6hgsBV4KfAW4F7g5Q2LTE2VmU7+kiRJ4149wwz+FHhFZv4uIvqABzPzmojYAJwO/LihEaoplj++lSe2dDO1vY2nLZrT7HAkSZIqqqdndhawpvT8cWDP0vNbgaMbEZSab2lp8tehe89l2hRHjkiSpPGp3hXADik9Xwq8OyL2AU4BVjcoLjVZ/xCDoxwvK0mSxrF6hhmcBywqPf808FPgjcA24OSGRKWm217JwPqykiRp/Ko5mc3My8qe3xwRTwaeBjyUmWsbGJuapLu3j1tXFsvYOvlLkiSNZ3XXme2XmVuA/2tALBon7n54I109fcydPoUn7z6r2eFIkiQNqapkNiLOqfaEmXla/eFoPOhf+evIfefT1hbNDUaSJGkY1fbMHlXlfllvIBo/rC8rSZJaRVXJbGa+cLQD0fixbHkxXtaVvyRJ0nhXT2kuTWCbunq4Z81GAI6wkoEkSRrnmp7MRsR7I+L+iOiMiJsi4nm72H9aRHw2Ih6MiK6I+H1EvG2s4p3oblu5nkzYZ/4MFsyZ3uxwJEmShjXiagYjEREnUdStfS9wLfBu4KqIOCwzHxrisCuBvYC3A/cBC2jy+5hIllpfVpIktZBmJ4GnARdn5kWl16dGxEuA9wCnD945Il4KvAA4MDMfLzU/MBaBThZO/pIkSa2kacMMImIqcAxw9aBNVwPPHuKwlwM3Ah+NiJURcU9E/FNEzBjmOtMiYm7/A5jTiPgnqu0rf81vahySJEnVqCuZjYg3R8S1EbEqIvYvtZ0aEa+o4TR7AO3AI4PaHwEWDnHMgcBzgWcArwJOBV4DnD/MdU4H1pc9VtQQ46SyZkMnq9Z30hZw+D4OM5AkSeNfzclsRLwHOAf4CTCfIiEFWEeRXNZqcG3aqNDWr6207Y2ZeUNm/oRiqMLJw/TOngXMK3ssriPGSWHZiqIk18F7zWHWtGaPQJEkSdq1enpmPwC8MzM/C/SWtd8IHF7DedaWjh/cC7uAnXtr+60GVmbm+rK2OykS4IpJamZ2ZeaG/gewsYYYJxXHy0qSpFZTTzJ7AHBzhfYuYFa1J8nMbcBNwPGDNh0PXDfEYdcCe0fE7LK2g4E+HD4wYksdLytJklpMPcns/cCSCu0vA+6o8VznAO+IiLdFxKERcS6wH3AhQEScFRGXlu3/XeAx4BsRcVhEPB/4PHBJZm6t8doq09eXLFuxDrAslyRJah31DIz8PHB+REyn+Hj/mRHxeoqJVu+o5USZeUVE7A58ClgE3AackJkPlnZZRJHc9u+/KSKOB75MMazhMYq6s39Xx/tQmfsf28zGzh6md7Rx8F4WfJAkSa2h5mQ2M78REVOAs4GZFL2lK4EPZebldZzvAuCCIbadXKHtLnYemqAR6h8v+4y959HR3vSF4SRJkqpS15T1zPw68PWI2ANoy8w1jQ1LY60/mV3ieFlJktRC6inNdUZEHASQmWtNZCeGpaWyXE7+kiRJraSez5NfDdwTEb+JiPdHxJ6NDkpjq6unlztXbQDsmZUkSa2l5mQ2M48AjgB+TrFgwcqI+ElEvCEiZjY6QI2+O1dvZFtvH0+aNZXFuw25MrAkSdK4U9dMn8y8PTM/npkHAi+kKNd1HvBwA2PTGNm+WMI8IqK5wUiSJNWgEdPWNwNbgW1ARwPOpzG2zMUSJElSi6ormY2IAyLiExFxB0W916OBM9l5aVq1gKWlxRIcLytJklpNzaW5IuJ64JnArcA3gO9m5spGB6axsX5rN394dDMARy6e39xgJEmSalRPndlfAO/IzNsbHYzG3i2lXtn9d5/JbrOmNjcYSZKkGtWzAtjHRyMQNcf2yV/zmxqHJElSPapKZiPiHOCTmbm59HxImXlaQyLTmFi63MUSJElS66q2Z/YotlcqOGqUYtEYy0yWDixjO6+5wUiSJNWhqmQ2M19Y6bla2+r1nazd1MWUtuDpe5vMSpKk1lNzaa6IuCQi5lRonxURlzQmLI2F/vGyT1s0h+kd7c0NRpIkqQ711Jl9C1BpzdMZwF+PLByNpaVO/pIkSS2u6moGETEXiNJjTkR0lm1uB04A1jQ2PI2mpa78JUmSWlwtpbnWAVl63FNhewJnNCAmjYHevuTWlUUlA1f+kiRJraqWZPaFFL2yPwdeDTxetm0b8GBmrmpgbBpF963ZxJZtvcya2s5Be85udjiSJEl1qTqZzcxfAUTEAcBDmZmjFpVGXf/kryMWz6e9LZobjCRJUp2qXTThCOC2zOwD5gGHR1ROgDLzlsaFp9GytLSMreNlJUlSK6u2Z3YpsJBigtdSivGxlbLZpJgMpnFu6UPrABdLkCRJra3aZPYA4NGy52phW7f1cvcjGwF7ZiVJUmurdgWwBys9V2u6fdV6evuSBXOmsXDu9GaHI0mSVLd6VgB7S0T8ednrsyNiXURcFxH7NzY8jYby+rJDjX2WJElqBfWsAPZxYCtARBwHvB/4KLAWOLdxoWm0LFthfVlJkjQx1FJntt++wH2l568E/i0zvxYR1wK/bFBcGkX9ZblMZiVJUqurp2d2E7B76fmLgZ+VnncCMxoRlEbPY5u6eOjxLQAcvthKBpIkqbXV0zN7DXBRRNwMHAz8uNT+dOCBBsWlUXJLaYjBQXvOYu70jiZHI0mSNDL19My+D7ge2BN4dWY+Vmo/BviXRgWm0VE++UuSJKnV1dwzm5nrKCZ9DW4/oxEBaXQtK6385XhZSZI0EdQzzICImA+8HTiUYtWvO4GLM3N940JTo2Wmk78kSdKEUk+d2WOB3wMfBp4E7FF6/vuIOLqx4amRlj++lSe2dDO1vY2nLZzb7HAkSZJGrJ6e2XOBHwLvzMwegIiYAlwEnAc8v2HRqaFuXv4EAIftPZepU+oZLi1JkjS+1JPMHktZIguQmT0RcTZwY8MiU8MtW+5iCZIkaWKpp3tuA7BfhfZ9gY0jC0ejqX/y15H7Wl9WkiRNDPUks1cAF0fESRGxb0QsjojXUQwzsDTXONXd28dtK4ue2SMXz29uMJIkSQ1SzzCDj1BUMLi07Phu4CvAxxoUlxrs7oc30tXTx9zpUzhgj1nNDkeSJKkh6qkzuw34UEScDhwEBHBfZm5pdHBqnO1DDOYTEc0NRpIkqUGqHmYQETMj4vyIWBkRayiGFazOzFtMZMc/68tKkqSJqJYxs58GTgZ+DFwOHE8xtEAtYGAZW8fLSpKkCaSWYQYnAm/PzMsBIuI7wLUR0Z6ZvaMSnRpiU1cP967ZBMARVjKQJEkTSC09s/sCv+5/kZk3AD3A3o0OSo1164r1ZMI+82ewYM70ZocjSZLUMLUks+3AtkFtPdRXEUFjqH/yl+NlJUnSRFNLIhrANyOiq6xtOnBhRGzub8jMExsVnBqjf/KXiyVIkqSJppZk9lsV2r7TqEA0epY5+UuSJE1QVSezmfnW0QxEo2PNhk5Wre+kLeAZ+9gzK0mSJpZ6lrNVC+kvyXXwXnOYNc3hzZIkaWKpKpmNiAsjYt8q9z0pIt44srDUKAMrfznEQJIkTUDVdtU9CtwWEdcBPwRuBFYBncBuwGHAc4HXASuBdzU+VNVj2fL1ACzZb35zA5EkSRoFVSWzmfnJiPgy8HbgFOAZg3bZCPwMeEdmXt3YEFWvvr60Z1aSJE1otUwAWwOcBZwVEfOB/YEZwFrg95mZoxKh6nb/Y5vZ2NnD9I42Dt5rdrPDkSRJari6ZgRl5jpgXUMjUcMtfWgdAIfvM48p7c71kyRJE48ZzgTmEANJkjTRmcxOYNtX/prf1DgkSZJGi8nsBNXV08sdqzcAsMRkVpIkTVAmsxPUnas30t2b7D5rKot3m9HscCRJkkZFXclsREyJiD+LiHdHxJxS294R4ZT5caJ8iEFENDcYSZKkUVJzNYOI2B/4L2A/YBpwDUWd2Y8C0ynq0KrJ+pexdfKXJEmayOrpmf0ixQpguwFby9q/D7yo1pNFxHsj4v6I6IyImyLieVUe95yI6ImIpbVeczLY3jM7r7mBSJIkjaJ6ktnnAv9fZm4b1P4gsE8tJ4qIk4DzgM8CRwG/Bq6KiP12cdw84FLgv2u53mSxfks3f1i7GbBnVpIkTWz1JLNtQHuF9sUUww1qcRpwcWZelJl3ZuapwHLgPbs47qvAd4Hrd3WBiJgWEXP7H8CcGmNsObesXAfAk3efyW6zpjY3GEmSpFFUTzJ7DXBq2essTfz6NPCTak8SEVOBY4CrB226Gnj2MMe9FTiodL1qnA6sL3usqDbGVmV9WUmSNFnUk8x+GHhBRNxBMeHru8ADFEMM/raG8+xB0cP7yKD2R4CFlQ6IiKcC/wi8MTN7qrzOWcC8ssfiGmJsSUuXrwccYiBJkia+mqsZZOaqiFgCvI6iZ7UNuBi4LDO3DnfsUKcc9DoqtBER7RSJ8xmZeU8N8XYBXWXnqSPE1pGZ2ysZ2DMrSZImuHpKcz0fuC4zvwF8o6x9SkQ8PzP/p8pTrQV62bkXdgE799ZCMdb1WOCoiPjnUltbcenoAV6cmT+v4a1MSKvWd7J2UxdT2oKn7z232eFIkiSNqnqGGfwCeFKF9nmlbVUpVUO4CTh+0KbjgesqHLIBOBxYUva4ELi79Py31V57IusfL/u0RXOY3lFpnp4kSdLEUXPPLEMMAwB2BzbXeK5zgG9HxI0UlQneRbEYw4UAEXEWsE9m/nVm9gG37RBIxBqgMzNvQ0DZ5C/Hy0qSpEmg6mQ2Iv699DSBb0ZEV9nmduAIKveoDikzr4iI3YFPAYsoktUTMvPB0i6LKJJbVal/vOwSx8tKkqRJoJae2fWlr0FRT7Z8stc24DfA12sNIDMvAC4YYtvJuzj2TODMWq85UfX2JbeuLG6TyawkSZoMqk5mM/OtABHxAPBPmVnrkAKNsnvXbGTLtl5mT5vCgXvObnY4kiRJo66e0lzVLlagMdY/XvbwfebR3jaxS5BJkiRBfRPAiIjXAH9FMZ51h/VSM/PoBsSlOgwsluAQA0mSNEnUXJorIj5IUV92DXAUcAPwGHAgcFVDo1NNlg1M/prX3EAkSZLGSD11Zt8LvCsz308x8evszDwe+BJFrVk1wdZtvdz9yEYAluy7W5OjkSRJGhv1JLP7sb0E11aKlbkAvg28vhFBqXa3r1pPb1+y19xpLJw3vdnhSJIkjYl6ktmHKRZIAHgQeFbp+QEUZbvUBEtdLEGSJE1C9SSzPwf+svT8YuDciLgGuAL4fqMCU20Gklknf0mSpEmknmoG76KUBGfmhRHxOPBc4EeUlqHV2Fu2Yh3gYgmSJGlyqafObB/QV/b6SuBKgIjYB1jZsOhUlcc2dbH88a1EwOGLnYMnSZImj3qGGewkIhZGxJeB+xpxPtXmlhVFfdmD9pzN3OkdTY5GkiRp7FSdzEbE/Ii4LCIejYhVEfHBiGiLiM8Af6CYCPa2UYtUQ3LylyRJmqxqGWbwD8DzgW8BLwXOLX2dDrwsM3/V+PBUjaUuliBJkiapWpLZPwfempk/i4gLKIYU3JOZp45KZKpKZg5M/rKSgSRJmmxqGTO7N3AHQGb+AegELhqNoFS9hx7fwrot3Uxtb+NpC+c2OxxJkqQxVUsy2wZ0l73uBTY3NhzVqn+IwWF7z2XqlIbM55MkSWoZtQwzCOCbEdFVej0duDAidkhoM/PERgWnXVu2vKhkYH1ZSZI0GdWSzH5r0OvvNDIQ1cfFEiRJ0mRWdTKbmW8dzUBUu+7ePm5bWfTMOvlLkiRNRg6ybGF3P7yRrp4+5k6fwpN3n9nscCRJksacyWwLG1gsYd/5RERzg5EkSWoCk9kWtmxgsYT5TY1DkiSpWUxmW5iTvyRJ0mRnMtuiNnX1cO+aTQAcsXh+c4ORJElqkrqS2Yh4c0RcGxGrImL/UtupEfGKxoanody6Yj2ZsM/8Gew5Z1qzw5EkSWqKmpPZiHgPcA7wE2A+0F7atA44tUFxaReWOl5WkiSprp7ZDwDvzMzPUixp2+9G4PCGRKVdWjZQyWBecwORJElqonqS2QOAmyu0dwGzRhaOqtU/+etIx8tKkqRJrJ5k9n5gSYX2lwF3jCgaVeWRDZ2sXt9JW8Dhi+2ZlSRJk1fVy9mW+TxwfkRMBwJ4ZkS8HjgdeEcjg1Nl/UMMDt5rDjOn1nMLJUmSJoaaM6HM/EZETAHOBmYC3wVWAh/KzMsbHJ8qsL6sJElSoa5uvcz8OvD1iNgDaMvMNY0NS8MpX8ZWkiRpMqunNNcZEXEQQGauNZEdW319yS3L1wNO/pIkSapnAtirgXsi4jcR8f6I2LPRQWlof1i7mY1dPczoaOfgvWY3OxxJkqSmqjmZzcwjgCOAnwOnASsj4icR8YaImNnoALWj/slfh+8zjyntrkYsSZImt7qyocy8PTM/npkHAi+kKNd1HvBwA2NTBQP1ZV0sQZIkqb5kdpDNwFZgG9DRgPNpGMuc/CVJkjSgrmQ2Ig6IiE9ExB0Uy9geDZwJLGxgbBqks7uXO1ZvAJz8JUmSBHWU5oqI64FnArcC3wC+m5krGx2Ydnbn6g109ya7z5rK4t1mNDscSZKkpqunzuwvgHdk5u2NDkbDKx9iEBHNDUaSJGkcqGcFsI+PRiDatWUrivqyrvwlSZJUqCqZjYhzgE9m5ubS8yFl5mkNiUw7cfKXJEnSjqrtmT2K7ZUKjhqlWDSM9Vu6+cPazQAcudiyXJIkSVBlMpuZL6z0XGOnv77sk3efyfyZU5sbjCRJ0jhRc2muiLgkIuZUaJ8VEZc0JiwN5hADSZKkndVTZ/YtQKW6UDOAvx5ZOBrKwMpf1peVJEkaUHU1g4iYC0TpMSciOss2twMnAGsaG54AMpOly0uVDPab39xgJEmSxpFaSnOtA7L0uKfC9gTOaEBMGmTV+k7WbupiSltw2KK5zQ5HkiRp3KglmX0hRa/sz4FXA4+XbdsGPJiZqxoYm0r6x8seumgu0zvamxuMJEnSOFJ1MpuZvwKIiAOAhzIzRy0q7WDpwOQvS3JJkiSVq3bRhCOA2zKzD5gHHD7UcqqZeUvjwhOUJbNO/pIkSdpBtT2zS4GFFBO8llKMj62UzSbFZDA1SE9vH7e6jK0kSVJF1SazBwCPlj3XGLnv0U1s7e5l9rQpHLTn7GaHI0mSNK5UuwLYg5Wea/T1T/46YvE82toqD+2QJEmarOpZAewtEfHnZa/Pjoh1EXFdROzf2PDUX1/Wlb8kSZJ2Vs8KYB8HtgJExHHA+4GPAmuBcxsXmsDJX5IkScOppc5sv32B+0rPXwn8W2Z+LSKuBX7ZoLgEbNnWwz2PbASc/CVJklRJPT2zm4DdS89fDPys9LwTmFHrySLivRFxf0R0RsRNEfG8YfY9MSKuiYhHI2JDRFwfES+p+R20iNtXbaC3L9lr7jQWzpve7HAkSZLGnXqS2WuAiyLiIuBg4Mel9qcDD9Ryoog4CTgP+CxwFPBr4KqI2G+IQ55fuv4JwDHAL4AfRcRRtb2F1tA/+cteWUmSpMrqSWbfB1wP7Am8OjMfK7UfA/xLjec6Dbg4My/KzDsz81RgOfCeSjtn5qmZeXZm/i4z783MjwP3An9Zx/sY97av/DW/qXFIkiSNVzWPmc3MdRSTvga3n1HLeSJiKkUC/I+DNl0NPLvKc7QBc4DHh9lnGjCtrGlOLXE207IV6wBY4uQvSZKkiuqZAEZEzAfeDhxKserXnRQ9rOtrOM0eFKuFPTKo/RGK1caq8TfALODKYfY5Hagp0R4PHtvUxfLHtxIBz1g8r9nhSJIkjUv11Jk9Fvg98GHgSRRJ6YeB30fE0XXEkIMvUaGtUhyvB84ETsrMNcPsehYwr+yxuI4Yx1x/r+xBe85m7vSO5gYjSZI0TtXTM3su8EPgnZnZAxARU4CLKCZzPb/K86wFetm5F3YBO/fW7qA0cexi4LWZ+bPh9s3MLqCr7Ngqw2uugcUSHGIgSZI0pHomgB0LfK4/kQUoPT+7tK0qmbkNuAk4ftCm44Hrhjqu1CP7TeANmfnjofZrddsrGTjEQJIkaSj19MxuAPYD7hrUvi+wscZznQN8OyJupKiQ8K7SuS8EiIizgH0y869Lr18PXAp8CPhNRPT36m6tcbzuuJaZ2yd/7btbc4ORJEkax+pJZq8ALo6Ij1D0oCbwXODz1FiaKzOviIjdgU8Bi4DbgBMy88HSLosoktt+7y7FfH7p0e9bwMk1v5Nx6qHHt7BuSzdTp7RxyMKWKb4gSZI05upJZj9CkcBeWnZ8N/AV4GO1niwzLwAuGGLbyYNe/0mt529F/fVln773XKZOqWckiCRJ0uRQT53ZbcCHIuJ04CCK6gP3ZeaWRgc3WQ0sluDkL0mSpGFV3e0XETMj4vyIWBkRayiqF6zOzFtMZBvLZWwlSZKqU8tn2J+mGJf6Y+ByiqoDXxmFmCa17t4+blu1AXAZW0mSpF2pZZjBicDbM/NygIj4DnBtRLRnZu+oRDcJ3f3wRrb19DFvRgdP3n1ms8ORJEka12rpmd0X+HX/i8y8AegB9m50UJPZwHjZfee3zAIPkiRJzVJLMtsObBvU1kN9FRE0hIHxsotdLEGSJGlXaklEA/hmRHSVtU0HLoyIzf0NmXlio4KbjMp7ZiVJkjS8WpLZb1Vo+06jAhFs7Ozmvkc3AXCEZbkkSZJ2qepkNjPfOpqBCG5duZ5M2Gf+DPacM63Z4UiSJI17Li81jixbvh6AJfvNb24gkiRJLcJkdhzZPvlrflPjkCRJahUms+PIshXrACd/SZIkVctkdpx4ZEMnq9d30hbwjH3mNjscSZKklmAyO070l+Q6eK85zJxq6V5JkqRq1JXMRsSbI+LaiFgVEfuX2k6NiFc0NrzJY2C8rEMMJEmSqlZzMhsR7wHOAX4CzKdYGQxgHXBqg+KadPrHy5rMSpIkVa+entkPAO/MzM8CvWXtNwKHNySqSaavL7mlVJbLyV+SJEnVqyeZPQC4uUJ7FzBrZOFMTn9Yu5mNXT3M6GjnqQtmNzscSZKkllFPMns/sKRC+8uAO0YUzSTVP/nr8H3mMaXdOXmSJEnVqmfa/OeB8yNiOhDAMyPi9cDpwDsaGdxk0T/568h95zU3EEmSpBZTczKbmd+IiCnA2cBM4LvASuBDmXl5g+ObFFwsQZIkqT51FTTNzK8DX4+IPYC2zFzT2LAmj87uXu5cvQGwkoEkSVKtRlSdPzPXNiqQyerO1Rvo7k32mD2VfebPaHY4kiRJLaXmZDYi7gdyqO2ZeeCIIppkBsbLLp5PRDQ3GEmSpBZTT8/seYNedwBHAS+lmBymGiwdmPw1v6lxSJIktaJ6JoB9sVJ7RLwPOHbEEU0yy1a4WIIkSVK9GlnU9Crg1Q0834S3bss27l+7GYAjF1uWS5IkqVaNTGZfAzzewPNNeLeUemUP2GMW82dObXI0kiRJraeeCWA3s+MEsAAWAnsC721QXJPC9slf9spKkiTVo54JYP8x6HUf8Cjwy8y8a8QRTSIuliBJkjQyNSWzpZW/HgB+mpkPj0pEk0RmWslAkiRphGoaM5uZPcBXgGmjE87ksXLdVtZu2saUtuCwRXObHY4kSVJLqmcC2G8p6spqBJYtLyZ/HbpoLtM72pscjSRJUmuqZ8zsBcAXImIxcBOwuXxjZt7SiMAmuv7xskscYiBJklS3qpPZiLgEOBW4otT0pbLNSVHVIAG7GavgeFlJkqSRq6Vn9i3Ax4ADRimWSaOnt49bSzVml+xrWS5JkqR61ZLMBkBmPjhKsUwa967ZxNbuXmZPm8KBe8xudjiSJEktq9YJYLnrXbQr/YslHLF4Hm1t0dxgJEmSWlitE8DuiYhhE9rMfNII4pkUXCxBkiSpMWpNZs8A1o9GIJPJ0uX942XnNzcQSZKkFldrMnt5Zq4ZlUgmiS3berjnkY2AyawkSdJI1TJm1vGyDXD7qg309iUL505nr7nTmx2OJElSS6slmXWmUgMsfWgdAEdakkuSJGnEqh5mkJn1LH2rQZY6+UuSJKlhTFDHWH9ZriWL5zc1DkmSpInAZHYMrd3UxYonthIBhy92mIEkSdJI1VrNQCNwS2mIwVP2nM2c6R3NDUaSpCbKTHp6eujt7W12KGqSjo4O2tvbR3wek9kx1F9f1vGykqTJbNu2baxevZotW7Y0OxQ1UUSwePFiZs+ePaLzmMyOof7xsiazkqTJqq+vj/vvv5/29nb23ntvpk6dSoQFkyabzOTRRx9lxYoVPPWpTx1RD63J7BjJzIFlbJ38JUmarLZt20ZfXx/77rsvM2fObHY4aqI999yTBx54gO7u7hEls04AGyMPPraFdVu6mTqljUMWzml2OJIkNVVbmynIZNeoHnl/ksZIf6/sM/aey9QpftslSZIawaxqjCx1vKwkSVLDmcyOkYHFEkxmJUmSGsZkdgx09/Zx26oNABzp5C9JklraddddR3t7Oy996Ut32vbLX/6SiGDdunU7bVuyZAlnnnnmDm0333wzr33ta9lrr72YPn06Bx98MO985zu55557Rin6wgUXXMABBxzA9OnTOeaYY/j1r3+9y2POP/98Dj30UGbMmMEhhxzCpZdeutM+3/ve9zjssMOYNm0ahx12GN///vdHI/wdmMyOgbtWb2RbTx/zZnSw/+7O3JQkqZVdcsklfOADH+B///d/eeihh+o+z3/+53/yrGc9i66uLi677DLuvPNOvv3tbzNv3jw++clPNjDiHV1xxRWceuqpfOITn+Dmm2/mec97Hi972cuGfS9f+cpXOP300znzzDO5/fbb+fSnP8373vc+fvSjHw3sc/3113PSSSfx5je/mWXLlvHmN7+Zv/qrv+K3v/3tqL0XgMjMUb3AeBMRc4H169evZ+7cuWNyzW//5kE++R+38fyD9+TStz1zTK4pSdJ41NnZyf333z/QKwhF+cqt3c1ZCWxGR3tNs+o3b97MokWL+N3vfscZZ5zBYYcdxqc+9amB7b/85S954QtfyBNPPMH8+fN3OHbJkiW88pWv5Mwzz2TLli3sv//+PPe5z63Ye7lu3bqdjm+UP/7jP+boo4/mK1/5ykDboYceyitf+UrOOuusisc8+9nP5jnPeQ6f//znB9pOPfVUbrzxRv73f/8XgJNOOokNGzZw1VVXDezz0pe+lN12241/+Zd/2emclX4W+m3YsIF58+YBzMvMDcO9H+vMjgHHy0qSNLSt3b0c9qmfNuXad3zmJcycWn06dMUVV3DIIYdwyCGH8KY3vYkPfOADfPKTn6y5zNRPf/pT1q5dy0c/+tGK24dLZE855RS+853vDHv+O+64g/3222+n9m3btnHTTTfxsY99bIf2F7/4xVx33XVDnq+rq2unhHPGjBnccMMNdHd309HRwfXXX8+HP/zhHfZ5yUtewnnnnTdsrCPV9GEGEfHeiLg/Ijoj4qaIeN4u9n9Bab/OiPhDRJwyVrHWa3syO6+5gUiSpBG5+OKLedOb3gQUvY6bNm3iv//7v2s+z7333gvA0572tJqP/cxnPsPSpUuHfey9994Vj127di29vb3stddeO7TvtddePPzww0Ne8yUveQkXXXQRN910E5nJjTfeyCWXXEJ3dzdr164F4OGHH675vI3Q1J7ZiDgJOA94L3At8G7gqog4LDN3GrgREQcAPwG+DrwJeA5wQUQ8mpnfG7PAa7Cxs5v7Ht0EwBFO/pIkaSczOtq54zMvadq1q3X33Xdzww038O///u8ATJkyhZNOOolLLrmEP/uzP6vpuiMZ5rlgwQIWLFhQ9/Gw84IFmTls7/InP/lJHn74YZ71rGeRmey1116cfPLJnH322Tus3lXreRuh2cMMTgMuzsyLSq9PjYiXAO8BTq+w/ynAQ5l5aun1nRFxLPARYFwms7euXE8mLN5tBnvMntbscCRJGncioqaP+pvl4osvpqenh3322WegLTPp6OjgiSeeYLfddhuYj7N+/fqdhgqsW7eufxwoBx98MAB33XUXxx13XE1xjGSYwR577EF7e/tOvaVr1qzZqVe13IwZM7jkkkv46le/yiOPPMKiRYv42te+xpw5c9hjjz0AWLhwYc3nbYSmDTOIiKnAMcDVgzZdDTx7iMOOq7D/T4FjI6JjiOtMi4i5/Q9gTNeSdbEESZJaX09PD5deeilf+MIXdvg4f9myZey///5cdtllADz1qU+lra2N3/3udzscv3r1alauXMkhhxwCFGNU99hjD84+++yK16tU2qvfSIYZTJ06lWOOOYZrrrlmh/ZrrrmGZz97qPRru46ODhYvXkx7ezuXX345f/EXfzGwNPFxxx2303mvvvrqqs47Es38b9AeQDvwyKD2R4CFQxyzcIj9p5TOt7rCMacDZ9Qf5sgcvs88Tjp2X5510JOaFYIkSRqh//zP/+SJJ57g7W9/+0Dvar/XvOY1XHzxxbz//e9nzpw5vPvd7+Zv/uZvmDJlCkceeSSrVq3iE5/4BIceeigvfvGLAZg1axYXXXQRr33ta3n5y1/OBz/4QZ7ylKewdu1arrzySh566CEuv/zyirGMdJjBaaedxpvf/GaOPfZYjjvuOL72ta/x0EMPccop26chnX766axcuXKgluw999zDDTfcwB//8R/zxBNPcM4553DbbbfxrW99a+CYD33oQzz/+c/nc5/7HK94xSv4wQ9+wM9+9rOBagejJjOb8gD2BhI4blD7J4C7hjjmHuD0QW3PKZ1n4RDHTAPmlj32AXL9+vUpSZLG1tatW/OOO+7IrVu3NjuUmvzFX/xFnnDCCRW33XTTTQnkTTfdlJmZnZ2d+ZnPfCYPPfTQnDFjRu6///558skn5+rVq3c69ne/+12eeOKJueeee+a0adPyKU95Sr7rXe/Ke++9d1Tfz/nnn5/7779/Tp06NY8++uj81a9+tcP2t7zlLfmCF7xg4PUdd9yRS5YsyRkzZuTcuXPzFa94Rd511107nfdf//Vf85BDDsmOjo582tOelt/73veGjGG4n4X169dnKb+bm7vIKZtWZ7Y0zGAL8NrM/H5Z+xeBJZn5ggrH/A9wc2Z+qKztVcCVwMzM7K7iumNeZ1aSJBWGqy2qyaVRdWabNmY2M7cBNwHHD9p0PDBUobPrK+z/YuDGahJZSZIkTSzNrjN7DvCOiHhbRBwaEecC+wEXAkTEWRFRvvDvhcD+EXFOaf+3AW8H/mnMI5ckSVLTNbUORmZeERG7A58CFgG3ASdk5oOlXRZRJLf9+98fEScA5wLvA1YBH8xxWmNWkiRJo6vpRd0y8wLggiG2nVyh7VfA0aMcliRJklpAs4cZSJKkSahZE9A1fjTqZ8BkVpIkjZmOjmKNoy1btjQ5EjXbtm3bAHZYDrceTR9mIEmSJo/29nbmz5/PmjVrAJg5cyYR0eSoNNb6+vp49NFHmTlzJlOmjCwdNZmVJEljauHCYqHP/oRWk1NbWxv77bffiP8zYzIrSZLGVESwaNEiFixYQHe3ZeInq6lTp9LWNvIRryazkiSpKdrb20c8XlJyApgkSZJalsmsJEmSWpbJrCRJklrWpB0zu2HDhmaHIEmSpApqydNisq3AERH7ACuaHYckSZJ2aXFmrhxuh8mYzAawN7BxDC87hyKBXjzG11VjeP9an/ew9XkPW5v3r/U14x7OAVblLpLVSTfMoPQNGTbDb7SyYsAbM9PxDS3G+9f6vIetz3vY2rx/ra9J97Cq6zgBTJIkSS3LZFaSJEkty2R2bHQBny59Vevx/rU+72Hr8x62Nu9f6xu393DSTQCTJEnSxGHPrCRJklqWyawkSZJalsmsJEmSWpbJrCRJklqWyWwDRMR7I+L+iOiMiJsi4nm72P8Fpf06I+IPEXHKWMWqymq5hxFxYkRcExGPRsSGiLg+Il4ylvFqZ7X+HpYd95yI6ImIpaMconahjn9Lp0XEZyPiwYjoiojfR8Tbxipe7aiO+/fGiFgWEVsiYnVEfCMidh+reLWjiHh+RPwoIlZFREbEK6s4ZlzkMyazIxQRJwHnAZ8FjgJ+DVwVEfsNsf8BwE9K+x0F/APwpYh49ZgErJ3Ueg+B5wPXACcAxwC/AH4UEUeNfrSqpI572H/cPOBS4L9HO0YNr857eCXwIuDtwCHA64G7RjdSVVLH38LnUvzuXQw8HXgt8EfARWMRryqaBSwD3l/NzuMpn7E01whFxG+B/8vM95S13Qn8R2aeXmH/zwEvz8xDy9ouBI7MzOPGImbtqNZ7OMQ5bgeuyMzPjFKYGka99zAiLgfuBXqBV2bmktGOVZXV8W/pS4HLgQMz8/Gxi1SV1HH/PgK8JzMPKmv7APDRzNx3LGLW0CIigVdl5n8Ms8+4yWfsmR2BiJhK0TN39aBNVwPPHuKw4yrs/1Pg2IjoaGyE2pU67+Hgc7QBcwD/oDZBvfcwIt4KHERRBFxNVOc9fDlwI/DRiFgZEfdExD9FxIxRDFUV1Hn/rgMWR8QJUdgLeA3w49GLVA02bvKZKWN5sQloD6AdeGRQ+yPAwiGOWTjE/lNK51vdyAC1S/Xcw8H+huLjmSsbGJeqV/M9jIinAv8IPC8zeyJidCPUrtTze3gg8FygE3hV6RwXAE8CHDc7tmq+f5l5XUS8EbgCmE7xN/CHwAdGMU411rjJZ+yZbYzBYzWiQtuu9q/UrrFT6z0sdop4PXAmcFJmrhmFuFS9qu5hRLQD3wXOyMx7xiIwVa2W38O20rY3ZuYNmfkT4DTgZHtnm6bq+xcRhwFfAj5D0av7UuAA4MLRDFANNy7yGXtmR2YtxVi7wf/zXMDO/1vp9/AQ+/cAjzU0OlWjnnsIDEx4uBh4bWb+bHTCUxVqvYdzgGOBoyLin0ttbUBERA/w4sz8+WgFq4rq+T1cDazMzPVlbXdS/DFdTDEWWmOjnvt3OnBtZn6+9PqWiNgM/Doi/i4z/ZRy/Bs3+Yw9syOQmduAm4DjB206nmI8UCXXV9j/xcCNmdnd2Ai1K3Xew/4e2W8Cb8hMx3g1UR33cANwOLCk7HEhcHfp+W9HJVANqc7fw2uBvSNidlnbwUAfsKLhQWpIdd6/mRT3qlxv6avjflrD+MlnMtPHCB7AScA2ijFahwLnApuA/UvbzwIuLdv/AGAzcE5p/7eVjn91s9/LZH3UcQ9fD3QD76X4X2n/Y16z38tkfdR6DyscfyawtNnvYzI/6vg9nA0sB/4VOIyiZN49wNeb/V4m46OO+3dy6d/R91CMf34O8Dvgt81+L5P1UfqdWlJ6JPDh0vP9hriH4yafcZjBCGXmFaUiz58CFgG3ASdk5oOlXRYB+5Xtf39EnEDxi/4+YBXwwcz83thGrn613kPg3RRDdM4vPfp9i+IfaI2xOu6hxpk6/i3dFBHHA1+mqGrwGMUkzL8b08AF1HX/vhkRcyhqmn4BWAf8HPjbsYxbOziWom56v3NKX/v/to3bfMY6s5IkSWpZjpmVJElSyzKZlSRJUssymZUkSVLLMpmVJElSyzKZlSRJUssymZUkSVLLMpmVJElSyzKZlSRJUssymZVaQEScHBHrmh1HvSLigYg4dRf7nBkRS8cmovElIv40Iu6KiDH5N3m83I96rhERGRGvHOF1vxkR/zGSczTK4Fgi4pcRcV7zItouIhZExKMRsU+zY5GGYzIrjZHSH62s8HjKOIjt5EExrY6IKyPigAZd4o+Ar5Vdr1JC8k/Aixp0vYoqvM9HIuJHEfH0Os6zroGhnQ18NjP7hohzQt4PVXQi8MmxvmilBD8z1wDfBj491vFItTCZlcbWf1Gsb13+uL+pEW23gSKevYE3AEuAH0ZE+0hPnJmPZuaWXeyzKTMfG+m1qlD+Pv8cmAX8OCKmjsG1dxIRzwaeCvzroE2T5X6oTGY+npkbmx1HmW8Ab4yI3ZodiDQUk1lpbHVl5sODHr0RcVpE3BoRmyNieURcEBGzhzpJRBwZEb+IiI0RsSEiboqIY8u2Pzsi/icitpbO96WImLWL2LIUz+rM/AVFb8wzgKeUzvmeiPh9RGyLiLsj4s2DYjozIh6KiK6IWBURXyrbNvCxdkQ8UGr+fqlH8IGy45eWnr8kIjojYv6ga3wpIn7VwPd5I3AusD9wSNl5h7wfEfEnFH/g55X1nJ5Z2jY1Is6OiJWlY39b2n84rwOuzszOYeKcyPej/Hx/FBHXRMTaiFgfEb+KiKMr7LooIq4qXef+iHjtoPPsExFXRMQTEfFYRPwgIp5cbRxDxPbLiPhyRJxXOu8jEfGuiJgVEd8o/S7+PiJeVnZMe0RcXIpxa+k+faiK65xX9npRRPy47L2+IQYNExnu57W0/eSIWFe6j3dGxKaI+K+IWFTafibwFuAVZT/TfwKQmbcCDwOvGsn3TxpNJrPS+NAHfJAiWXkL8KcUHz0P5TJgBcXHxccA/wh0A0TE4cBPgX8HjgBOAp4L/HONMW0tfe2IiFcBXwS+UIrxq8A3IuKFpWu+Bvgw8G6KXsZXArcOcd4/Kn19K0XP4x9V2OdnwDrg1f0NUfRI/hXFe2/I+ywlZ28ovewu2zTc/bgOOJXtPaeLKD6ShyLJfQ5FgnoERW/rf0XEU4cJ4/nAjVWEO+HvBzAH+BbwPOBZwL3ATyJizqD9/h74HnAk8B3gXyLi0FIcM4FfAJsovrfPLT3/rxii972U7GUV8b0FWAs8E/gy8BWKe3wdcDTF+/92KQYo/sauoPg+HQZ8BviHiPirKq7V71KK3vk/ofj+vwtYMGifav79mAl8BHgzxfdlP7b/3P4TcCU7fnJ0XdmxN1DcE2l8ykwfPnyMwQP4JtBD8Ye1//GvQ+z7WmBt2euTgXVlrzcAbxni2EuBrw5qey7QC0wf4pjB518MXA8sB6YC1wJfG3TMlcCPS89PA+4GOoY4/wPAqWWvE3jloH3OBJaWvf4i8N9lr18MdAG7jfB9Zul7v7n0PIEf7OLeDXs/Sm0HUSQVew9q/xnwD8Ocex3w5kl6P3a4RoXt7aWf9b8YFOtXBu33G+CC0vO3AXcBUbZ9KrAFeHHZ7+J/lG1/FXDXLn4Gfgn8elBsm4BLy9oWluJ71jDnOR/4t7LXg2P5JXBe6fnTSuc7tmz7U0ptpw5zjUo/rwkcVNb2XuDhoeIYdL5zgF8M9/3x4aOZD3tmpbH1C4qxj/2PDwJExAtLH6+ujIiNFInB7sN8RHsOcFFE/CwiPhYRB5VtOwY4ufRR4qaI2ETRY9QGDDeBaF5p/81sT5pOzMxtwKEUCVS5a0vtUPROzQD+EBFfj4hXRcSUXX43hncZ8CcRsXfp9RuBn2TmEyN8nxspvvfHAKcAvy99HVDH/YCiZy6AewbF9AKKRHcoM4DBQwxg8tyPAVHMnr8wIu6JiPXAemA2RS9iuesrvO5/78dQJHwby+J4HJjOEPchM7+fmU+rIsRbyo7pBR5jxx7vR0pfB3pOI+KUiLgxiqoAm4B3Vng/QzmE4j/A/1d23fuAJ8p3qvLndUtm/r7s9Wp27uEdylaKnl1pXBrpP26SarO59MdoQETsD/wEuJBiFvPjFD1aFwMdlU6SmWdGxHcpJjC9DPh0RLwuM79PkTx8FfhShUMfGia2jRQJWR/wSGZuHnzZQa+jvy0zl0fEIcDxwJ8BFwD/LyJekJnd1CEzb4iI3wOvi4ivUPSevbVsl3rfZ1/ZPbgrIhYCV1B89FrX/SiLp5cimeodtG3TMMetBSpNrpks96PcN4E9KYZxPEjR83s9RSK/yxDL4riJItke7NEq4xjK4O9dlrdlZkZEfwyUhhOcC/wNxfvYCPw/4I+rvF7sqr2Gn9dKsQ91/sGexMi/d9KoMZmVmu9Yit/Fv8ntpZl2OaYuM+8B7gHOjYh/oUgsvk/Ri/P0wUlzFfqGOeZOij+Ql5a1PbvU3h/PVuCHFDPuz6f4qPdwynqVynRTfEy7K9+lSEpWUCR1Py7bVu/7HOxc4LSIeFXpPwPV3I9tFeK/udS2IDN/XcP1b6YYTznYZLwfzwPem5k/AYiIfYE9Kuz3LHZ878+i+D72x3ESsCYzN4wglkZ4HnBdZl7Q3zDoU5RduYviZ/EoigSdKEr5zS/bp65/Pyqo9DPd7xkUwx+kcclhBlLz/Z7ij9EHIuLAKGalnzLUzhExIyL+OSL+JCL2j4jnUEza6U9kPgccFxHnR8SSiHhqRLw8Ir48ghg/T/ER8iml851GUQ/zn0oxnRwRb4+IZ0TEgRSTTLZS9K5V8gDwoohYGMOX/LmMonfyExTjDMs/jm/I+ywlPBdR9G4H1d2PB4DZEfGiiNgjImaW/nNxGXBpRJwYEQdEMTv/byPihGFC+ClFYlqLiXo/7gPeHBGHRsQfl663tcJ+r42It0XEwRHxaYoJWf0TzS6j6O3+QUQ8r3QfXhARX4yIxZUuWhqGcVcNcdbyfo6NoorAwRHx91SeYFdRZt5FMeb6axHxzIg4iqI+8Fa290TX9O/HMB4AjoiIQ0o/0x0wMKHuGODqOs4pjQmTWanJMnMpxYSdvwVuo+j5On2YQ3qB3Sl6pu6hmPhzFXBG6Xy3UIzTfCrwa4oeq7+nGCNXb4z/AXyI4iPS2ylmyb81M39Z2mUdxVjAaynGFb4I+Mscuk7p31B8BL6c7T1qla57L/A7itnxlw3a1sj3+UWKMZevreZ+ZOZ1FB/rXkHx8etHS5veSnFfvkAxAeuHFB8pLx/m2t8BDisNC6jKBL4fb6MYcnEzRbH+LwFrKux3BkXFiFsoZu+/MTPvKMWxhWLIyEMUlRXuBC6hGEM8VE/tPMpKszXQhaUYrgB+S/F7e8GwR+zsrynG4v4PxScvX6cYrtAJdf37MZSvU/zM3kjxM/2cUvsrgIdq/LRBGlORWU01EknSaImIs4F5mfnuZsei8a3Uu7wc+LPM/O8xuN4NFNUVvjva15LqZc+sJDXfZ4EHowGre2liiYg/LQ3XOCCK1eIupxgS8D9jcO0FwL8B/zLa15JGwp5ZSZLGqYh4CcWwlQMphhdcR1Fjdqjxz9KkYzIrSZKkluUwA0mSJLUsk1lJkiS1LJNZSZIktSyTWUmSJLUsk1lJkiS1LJNZSZIktSyTWUmSJLUsk1lJkiS1rP8fV56XDMo9ixgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score, RocCurveDisplay\n",
    "\n",
    "auc = roc_auc_score(np.where(test_y == \"benign\", 0, 1), np.where(pred_y == \"benign\", 0, 1)) # AUC面积\n",
    "# 假阳性率，真阳性率\n",
    "fpr, tpr, thresholds = roc_curve(np.where(test_y == \"benign\", 0, 1), np.where(pred_y == \"benign\", 0, 1), pos_label=1)\n",
    "display = RocCurveDisplay(fpr, tpr, auc, pos_label=\"maligant\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5), dpi=100)\n",
    "display.plot(ax=ax)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "变量重要性:\n",
      " [[-1.68676979  3.24635523  1.73703209  0.85504022 -4.41062767  3.59637683\n",
      "  -1.99297205  2.22994614 -1.64142479]]\n",
      "正确率(Accuracy): 0.90\n",
      "准确率(Precision): 0.81\n",
      "灵敏度(Sensitivity)/召回率(recall): 0.92\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix\n",
    "\n",
    "print(\"变量重要性:\\n\", regr.coef_) # 绝对值越大，重要性越显著\n",
    "print(\"正确率(Accuracy): %.2f\" % accuracy_score(test_y, pred_y))\n",
    "print(\"准确率(Precision): %.2f\" % precision_score(test_y, pred_y, pos_label=\"malignant\"))\n",
    "print(\"灵敏度(Sensitivity)/召回率(recall): %.2f\" % recall_score(test_y, pred_y, pos_label=\"malignant\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**手动计算其它指标：**<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8814814814814815"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.11851851851851852"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.08"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.8952380952380953"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.8117647058823529"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.92"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(test_y, pred_y)\n",
    "tn = cm[0,0]\n",
    "tp = cm[1,1]\n",
    "fn = cm[1,0]\n",
    "fp = cm[0,1]\n",
    "\n",
    "tn/(tn + fp) # specificy, 特异度，真阴性率\n",
    "fp/(fp + tn) # fall out, 假阳性率\n",
    "fn/(fn + tp) # miss rate，假阴性率\n",
    "(tp + tn)/(tp + tn + fn + fp) # accuracy, 正确率\n",
    "tp/(tp + fp) # precision, 准确率\n",
    "tp/(tp + fn) # recall, 真阳性率，灵敏度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 前列腺数据\n",
    "[返回引言](#top)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据探索\n",
    "这里读取前列腺数据集，`gleason`表示患者的Gleason评分， 评分越高，病情越严重。<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lcavol</th>\n",
       "      <th>lweight</th>\n",
       "      <th>age</th>\n",
       "      <th>lbph</th>\n",
       "      <th>svi</th>\n",
       "      <th>lcp</th>\n",
       "      <th>gleason</th>\n",
       "      <th>pgg45</th>\n",
       "      <th>lpsa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.579818</td>\n",
       "      <td>2.769459</td>\n",
       "      <td>50</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.430783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.994252</td>\n",
       "      <td>3.319626</td>\n",
       "      <td>58</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.162519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.510826</td>\n",
       "      <td>2.691243</td>\n",
       "      <td>74</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>-0.162519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.203973</td>\n",
       "      <td>3.282789</td>\n",
       "      <td>58</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.162519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.751416</td>\n",
       "      <td>3.432373</td>\n",
       "      <td>62</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.371564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     lcavol   lweight  age      lbph  svi       lcp  gleason  pgg45      lpsa\n",
       "0 -0.579818  2.769459   50 -1.386294    0 -1.386294        6      0 -0.430783\n",
       "1 -0.994252  3.319626   58 -1.386294    0 -1.386294        6      0 -0.162519\n",
       "2 -0.510826  2.691243   74 -1.386294    0 -1.386294        7     20 -0.162519\n",
       "3 -1.203973  3.282789   58 -1.386294    0 -1.386294        6      0 -0.162519\n",
       "4  0.751416  3.432373   62 -1.386294    0 -1.386294        6      0  0.371564"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lcavol</th>\n",
       "      <th>lweight</th>\n",
       "      <th>age</th>\n",
       "      <th>lbph</th>\n",
       "      <th>svi</th>\n",
       "      <th>lcp</th>\n",
       "      <th>gleason</th>\n",
       "      <th>pgg45</th>\n",
       "      <th>lpsa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2.830268</td>\n",
       "      <td>3.876396</td>\n",
       "      <td>68</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>1</td>\n",
       "      <td>1.321756</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>4.385147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>3.821004</td>\n",
       "      <td>3.896909</td>\n",
       "      <td>44</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>1</td>\n",
       "      <td>2.169054</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>4.684443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2.907447</td>\n",
       "      <td>3.396185</td>\n",
       "      <td>52</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>1</td>\n",
       "      <td>2.463853</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>5.143124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2.882564</td>\n",
       "      <td>3.773910</td>\n",
       "      <td>68</td>\n",
       "      <td>1.558145</td>\n",
       "      <td>1</td>\n",
       "      <td>1.558145</td>\n",
       "      <td>7</td>\n",
       "      <td>80</td>\n",
       "      <td>5.477509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>3.471966</td>\n",
       "      <td>3.974998</td>\n",
       "      <td>68</td>\n",
       "      <td>0.438255</td>\n",
       "      <td>1</td>\n",
       "      <td>2.904165</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>5.582932</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      lcavol   lweight  age      lbph  svi       lcp  gleason  pgg45      lpsa\n",
       "92  2.830268  3.876396   68 -1.386294    1  1.321756        7     60  4.385147\n",
       "93  3.821004  3.896909   44 -1.386294    1  2.169054        7     40  4.684443\n",
       "94  2.907447  3.396185   52 -1.386294    1  2.463853        7     10  5.143124\n",
       "95  2.882564  3.773910   68  1.558145    1  1.558145        7     80  5.477509\n",
       "96  3.471966  3.974998   68  0.438255    1  2.904165        7     20  5.582932"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "lcavol     float64\n",
       "lweight    float64\n",
       "age          int64\n",
       "lbph       float64\n",
       "svi          int64\n",
       "lcp        float64\n",
       "gleason      int64\n",
       "pgg45        int64\n",
       "lpsa       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(\"F:/py_input_output/data_input/prostate.csv\").drop(\"train\", axis=1)\n",
    "df1.head()\n",
    "df1.tail()\n",
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**缺失值检验：**<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isna().all(axis=None) # 是否存在缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df1.agg(lambda x: np.sum(pd.isna(x))) # 各列缺失值数量\n",
    "# df1.agg(lambda x: np.sum(pd.isna(x))/len(x)) # 各列缺失值比例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果显示没有缺失值。<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**平衡性检验:**<br/>\n",
    "通过频率分布直方图，检验预测变量的平衡性。<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    56\n",
       "6    35\n",
       "9     5\n",
       "8     1\n",
       "Name: gleason, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.gleason.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100, figsize=(6, 4)) # 设置分辨率和尺寸大小\n",
    "plt.hist(df1[\"gleason\"])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**结果发现：**<br/>\n",
    "预测变量表面看起来是连续变量，实际上是离散变量。考虑到`gleason==8`只有1个观测值，以及业务特性，`gleason`值越高，病情越严重，不妨将gleason值为6设为0，其它都为1。<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lcavol</th>\n",
       "      <th>lweight</th>\n",
       "      <th>age</th>\n",
       "      <th>lbph</th>\n",
       "      <th>svi</th>\n",
       "      <th>lcp</th>\n",
       "      <th>gleason</th>\n",
       "      <th>pgg45</th>\n",
       "      <th>lpsa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2.830268</td>\n",
       "      <td>3.876396</td>\n",
       "      <td>68</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>1</td>\n",
       "      <td>1.321756</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>4.385147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>3.821004</td>\n",
       "      <td>3.896909</td>\n",
       "      <td>44</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>1</td>\n",
       "      <td>2.169054</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>4.684443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2.907447</td>\n",
       "      <td>3.396185</td>\n",
       "      <td>52</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>1</td>\n",
       "      <td>2.463853</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5.143124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2.882564</td>\n",
       "      <td>3.773910</td>\n",
       "      <td>68</td>\n",
       "      <td>1.558145</td>\n",
       "      <td>1</td>\n",
       "      <td>1.558145</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>5.477509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>3.471966</td>\n",
       "      <td>3.974998</td>\n",
       "      <td>68</td>\n",
       "      <td>0.438255</td>\n",
       "      <td>1</td>\n",
       "      <td>2.904165</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>5.582932</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      lcavol   lweight  age      lbph  svi       lcp  gleason  pgg45      lpsa\n",
       "92  2.830268  3.876396   68 -1.386294    1  1.321756        1     60  4.385147\n",
       "93  3.821004  3.896909   44 -1.386294    1  2.169054        1     40  4.684443\n",
       "94  2.907447  3.396185   52 -1.386294    1  2.463853        1     10  5.143124\n",
       "95  2.882564  3.773910   68  1.558145    1  1.558145        1     80  5.477509\n",
       "96  3.471966  3.974998   68  0.438255    1  2.904165        1     20  5.582932"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"gleason\"] = np.where(df1[\"gleason\"] == 6, 0, 1)\n",
    "df1.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**线性关系检验:**<br/>\n",
    "通过散点图检验自变量与因变量之间的线性关系。<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1500x2100 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=150, figsize=(10, 14)) # 设置分辨率和尺寸大小\n",
    "names = df1.columns.difference([\"gleason\"])\n",
    "\n",
    "for i in range(len(names)):\n",
    "    plt.subplot(4, 2, i+1)\n",
    "    plt.scatter(df1[names[i]], df1[\"gleason\"])\n",
    "    plt.title(\"gleason-{}\".format(names[i]))\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可用看出，规律非常明显，实际上属于相当于自变量都是连续变量的逻辑回归。<br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 划分训练集与测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lcavol</th>\n",
       "      <th>lweight</th>\n",
       "      <th>age</th>\n",
       "      <th>lbph</th>\n",
       "      <th>svi</th>\n",
       "      <th>lcp</th>\n",
       "      <th>pgg45</th>\n",
       "      <th>lpsa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>-0.446287</td>\n",
       "      <td>4.408547</td>\n",
       "      <td>69</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>0</td>\n",
       "      <td>2.962692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1.467874</td>\n",
       "      <td>3.070376</td>\n",
       "      <td>66</td>\n",
       "      <td>0.559616</td>\n",
       "      <td>0</td>\n",
       "      <td>0.223144</td>\n",
       "      <td>40</td>\n",
       "      <td>3.516013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.203973</td>\n",
       "      <td>3.282789</td>\n",
       "      <td>58</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.162519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.994252</td>\n",
       "      <td>3.319626</td>\n",
       "      <td>58</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.162519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2.999226</td>\n",
       "      <td>3.849083</td>\n",
       "      <td>69</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>1</td>\n",
       "      <td>1.909542</td>\n",
       "      <td>20</td>\n",
       "      <td>3.275256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      lcavol   lweight  age      lbph  svi       lcp  pgg45      lpsa\n",
       "68 -0.446287  4.408547   69 -1.386294    0 -1.386294      0  2.962692\n",
       "80  1.467874  3.070376   66  0.559616    0  0.223144     40  3.516013\n",
       "3  -1.203973  3.282789   58 -1.386294    0 -1.386294      0 -0.162519\n",
       "1  -0.994252  3.319626   58 -1.386294    0 -1.386294      0 -0.162519\n",
       "74  2.999226  3.849083   69 -1.386294    1  1.909542     20  3.275256"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lcavol</th>\n",
       "      <th>lweight</th>\n",
       "      <th>age</th>\n",
       "      <th>lbph</th>\n",
       "      <th>svi</th>\n",
       "      <th>lcp</th>\n",
       "      <th>pgg45</th>\n",
       "      <th>lpsa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.477049</td>\n",
       "      <td>2.998229</td>\n",
       "      <td>67</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>5</td>\n",
       "      <td>1.348073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2.034706</td>\n",
       "      <td>3.917011</td>\n",
       "      <td>66</td>\n",
       "      <td>2.008214</td>\n",
       "      <td>1</td>\n",
       "      <td>2.110213</td>\n",
       "      <td>60</td>\n",
       "      <td>2.882004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1.997418</td>\n",
       "      <td>3.719651</td>\n",
       "      <td>63</td>\n",
       "      <td>1.619388</td>\n",
       "      <td>1</td>\n",
       "      <td>1.909542</td>\n",
       "      <td>40</td>\n",
       "      <td>2.853592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.542324</td>\n",
       "      <td>4.178226</td>\n",
       "      <td>70</td>\n",
       "      <td>0.438255</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>20</td>\n",
       "      <td>2.806386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1.771557</td>\n",
       "      <td>3.896909</td>\n",
       "      <td>61</td>\n",
       "      <td>-1.386294</td>\n",
       "      <td>0</td>\n",
       "      <td>0.810930</td>\n",
       "      <td>6</td>\n",
       "      <td>2.374906</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      lcavol   lweight  age      lbph  svi       lcp  pgg45      lpsa\n",
       "13  1.477049  2.998229   67 -1.386294    0 -1.386294      5  1.348073\n",
       "63  2.034706  3.917011   66  2.008214    1  2.110213     60  2.882004\n",
       "61  1.997418  3.719651   63  1.619388    1  1.909542     40  2.853592\n",
       "58  0.542324  4.178226   70  0.438255    0 -1.386294     20  2.806386\n",
       "43  1.771557  3.896909   61 -1.386294    0  0.810930      6  2.374906"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "68    0\n",
       "80    1\n",
       "3     0\n",
       "1     0\n",
       "74    1\n",
       "Name: gleason, dtype: int32"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "13    1\n",
       "63    1\n",
       "61    1\n",
       "58    1\n",
       "43    1\n",
       "Name: gleason, dtype: int32"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_X, test_X, train_y, test_y = train_test_split(df1.drop(\"gleason\", axis=1), df1[\"gleason\"], \n",
    "                                                    test_size=0.3, random_state=126)\n",
    "\n",
    "train_X.tail()\n",
    "test_X.tail()\n",
    "\n",
    "train_y.tail()\n",
    "test_y.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 标准化\n",
    "标准化前必须处理缺失值。<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.03215436,  0.05517895,  0.9883288 ,  0.02947629,  0.        ,\n",
       "        -0.01756557,  0.12670882,  0.04353207],\n",
       "       [-0.00644613,  0.06367665,  0.99662963, -0.02002351,  0.        ,\n",
       "        -0.02002351,  0.        ,  0.04279286],\n",
       "       [ 0.01898135,  0.03970359,  0.85345807,  0.00723649,  0.        ,\n",
       "         0.00288551,  0.51724732,  0.04546621],\n",
       "       [-0.02070867,  0.05646488,  0.99761612, -0.02384465,  0.        ,\n",
       "        -0.02384465,  0.        , -0.00279537],\n",
       "       [-0.01710197,  0.05710033,  0.99764826, -0.02384542,  0.        ,\n",
       "        -0.02384542,  0.        , -0.00279546]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.03014097,  0.05554136,  0.98717885,  0.03163871,  0.        ,\n",
       "        -0.00590639,  0.13710817,  0.04062093],\n",
       "       [ 0.0219435 ,  0.04454263,  0.99537293, -0.02059522,  0.        ,\n",
       "        -0.02059522,  0.07428156,  0.0200274 ],\n",
       "       [ 0.02275827,  0.04381193,  0.73821278,  0.02246196,  0.01118504,\n",
       "         0.02360282,  0.67110253,  0.03223533],\n",
       "       [ 0.02668629,  0.049696  ,  0.8417048 ,  0.02163566,  0.01336039,\n",
       "         0.02551224,  0.53441574,  0.03812512],\n",
       "       [ 0.00742998,  0.05724273,  0.95901737,  0.0060042 ,  0.        ,\n",
       "        -0.01899258,  0.27400496,  0.03844819]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import Normalizer\n",
    "\n",
    "transformer = Normalizer().fit(train_X)\n",
    "train_X_normalized = transformer.transform(train_X)# 对训练集进行标准化\n",
    "test_X_normalized = transformer.transform(test_X)# 对测试集进行标准化\n",
    "\n",
    "train_X_normalized[-6:-1]\n",
    "test_X_normalized[-6:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "regr = linear_model.LogisticRegression()\n",
    "regr.fit(train_X_normalized, train_y) # 训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 1, 0])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_y = regr.predict(test_X)\n",
    "pred_y[:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 评价模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10,  0],\n",
       "       [ 6, 14]], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.62      1.00      0.77        10\n",
      "           1       1.00      0.70      0.82        20\n",
      "\n",
      "    accuracy                           0.80        30\n",
      "   macro avg       0.81      0.85      0.80        30\n",
      "weighted avg       0.88      0.80      0.81        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report, RocCurveDisplay\n",
    "confusion_matrix(test_y, pred_y)\n",
    "print(classification_report(test_y, pred_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:70: FutureWarning: Pass fpr=[0. 0. 1.], tpr=[0.  0.7 1. ], roc_auc=0.85 as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error\n",
      "  warnings.warn(f\"Pass {args_msg} as keyword args. From version \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score, RocCurveDisplay\n",
    "\n",
    "auc = roc_auc_score(test_y, pred_y) # AUC面积\n",
    "# 假阳性率，真阳性率\n",
    "fpr, tpr, thresholds = roc_curve(test_y, pred_y, pos_label=1)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5), dpi=100)\n",
    "display = RocCurveDisplay(fpr, tpr, auc, pos_label=\"gleason=7\")\n",
    "display.plot(ax=ax)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "变量重要性:\n",
      " [[ 0.1742922  -0.05683662 -0.82358711  0.08052062  0.03886027  0.18296661\n",
      "   3.28300943  0.13551456]]\n",
      "正确率(Accuracy): 0.80\n",
      "准确率(Precision): 1.00\n",
      "灵敏度(Sensitivity)/召回率(recall): 0.70\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix\n",
    "\n",
    "print(\"变量重要性:\\n\", regr.coef_) # 绝对值越大，重要性越显著\n",
    "print(\"正确率(Accuracy): %.2f\" % accuracy_score(test_y, pred_y))\n",
    "print(\"准确率(Precision): %.2f\" % precision_score(test_y, pred_y, pos_label=1))\n",
    "print(\"灵敏度(Sensitivity)/召回率(recall): %.2f\" % recall_score(test_y, pred_y, pos_label=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**手动计算其它指标：**<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.3"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.7"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(test_y, pred_y)\n",
    "tn = cm[0,0]\n",
    "tp = cm[1,1]\n",
    "fn = cm[1,0]\n",
    "fp = cm[0,1]\n",
    "\n",
    "tn/(tn + fp) # specificy, 特异度，真阴性率\n",
    "fp/(fp + tn) # fall out, 假阳性率\n",
    "fn/(fn + tp) # miss rate，假阴性率\n",
    "(tp + tn)/(tp + tn + fn + fp) # accuracy, 正确率\n",
    "tp/(tp + fp) # precision, 准确率\n",
    "tp/(tp + fn) # recall, 真阳性率，灵敏度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 糖尿病数据集\n",
    "[返回引言](#top)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>npreg</th>\n",
       "      <th>glu</th>\n",
       "      <th>bp</th>\n",
       "      <th>skin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>ped</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>2</td>\n",
       "      <td>141</td>\n",
       "      <td>58</td>\n",
       "      <td>34</td>\n",
       "      <td>25.4</td>\n",
       "      <td>0.699</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>7</td>\n",
       "      <td>129</td>\n",
       "      <td>68</td>\n",
       "      <td>49</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0.439</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>0</td>\n",
       "      <td>106</td>\n",
       "      <td>70</td>\n",
       "      <td>37</td>\n",
       "      <td>39.4</td>\n",
       "      <td>0.605</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
       "      <td>58</td>\n",
       "      <td>36</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.261</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>8</td>\n",
       "      <td>155</td>\n",
       "      <td>62</td>\n",
       "      <td>26</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.543</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     npreg  glu  bp  skin   bmi    ped  age\n",
       "195      2  141  58    34  25.4  0.699   24\n",
       "196      7  129  68    49  38.5  0.439   43\n",
       "197      0  106  70    37  39.4  0.605   22\n",
       "198      1  118  58    36  33.3  0.261   23\n",
       "199      8  155  62    26  34.0  0.543   46"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>npreg</th>\n",
       "      <th>glu</th>\n",
       "      <th>bp</th>\n",
       "      <th>skin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>ped</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>2</td>\n",
       "      <td>88</td>\n",
       "      <td>58</td>\n",
       "      <td>26</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.766</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>9</td>\n",
       "      <td>170</td>\n",
       "      <td>74</td>\n",
       "      <td>31</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.403</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     npreg  glu  bp  skin   bmi    ped  age\n",
       "327      2   88  58    26  28.4  0.766   22\n",
       "328      9  170  74    31  44.0  0.403   43\n",
       "329     10  101  76    48  32.9  0.171   63\n",
       "330      5  121  72    23  26.2  0.245   30\n",
       "331      1   93  70    31  30.4  0.315   23"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "195     No\n",
       "196    Yes\n",
       "197     No\n",
       "198     No\n",
       "199    Yes\n",
       "Name: type, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "327     No\n",
       "328    Yes\n",
       "329     No\n",
       "330     No\n",
       "331     No\n",
       "Name: type, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv(\"F:/py_input_output/data_input/Pima_tr.csv\") # 糖尿病数据集\n",
    "test_df = pd.read_csv(\"F:/py_input_output/data_input/Pima_te.csv\")\n",
    "\n",
    "train_X = train_df.drop(\"type\", axis=1)\n",
    "test_X = test_df.drop(\"type\", axis=1)\n",
    "\n",
    "train_y = train_df[\"type\"]\n",
    "test_y = test_df[\"type\"]\n",
    "\n",
    "train_X.tail()\n",
    "test_X.tail()\n",
    "train_y.tail()\n",
    "test_y.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**平衡性检验:**<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     132\n",
       "Yes     68\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "No     223\n",
       "Yes    109\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y.value_counts() \n",
    "test_y.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**缺失值检验:**<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X.isna().all(axis=None) # 是否存在缺失值\n",
    "test_X.isna().all(axis=None)\n",
    "train_y.isna().all()\n",
    "test_y.isna().all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_X.agg(lambda x: np.sum(pd.isna(x))) # 各列缺失值数量\n",
    "# train_X.agg(lambda x: np.sum(pd.isna(x))/len(x)) # 各列缺失值比例\n",
    "\n",
    "# test_X.agg(lambda x: np.sum(pd.isna(x))) # 各列缺失值数量\n",
    "# test_X.agg(lambda x: np.sum(pd.isna(x))/len(x)) # 各列缺失值比例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 标准化\n",
    "标准化前必须处理缺失值。<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00610125, 0.85417484, 0.45149241, 0.15863247, 0.1470401 ,\n",
       "        0.00505183, 0.14032872],\n",
       "       [0.0124935 , 0.88079168, 0.36231147, 0.21238948, 0.15866744,\n",
       "        0.00436648, 0.14992199],\n",
       "       [0.04256405, 0.78439467, 0.41347936, 0.29794836, 0.23410229,\n",
       "        0.00266937, 0.26146489],\n",
       "       [0.        , 0.75827093, 0.50074495, 0.26467947, 0.28184787,\n",
       "        0.00432787, 0.15737698],\n",
       "       [0.00703198, 0.8297737 , 0.40785487, 0.2531513 , 0.23416495,\n",
       "        0.00183535, 0.16173555]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[0.00593889, 0.76017802, 0.52262239, 0.23161674, 0.21676951,\n",
       "        0.00627741, 0.21973896],\n",
       "       [0.01748801, 0.76947226, 0.50715217, 0.22734408, 0.24832968,\n",
       "        0.00669791, 0.19236806],\n",
       "       [0.04545503, 0.85859503, 0.37374137, 0.15656733, 0.2222246 ,\n",
       "        0.00203538, 0.21717404],\n",
       "       [0.06532678, 0.65980048, 0.49648353, 0.31356854, 0.21492511,\n",
       "        0.00111709, 0.41155871],\n",
       "       [0.03373646, 0.81642223, 0.48580497, 0.1551877 , 0.17677903,\n",
       "        0.00165309, 0.20241874]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import Normalizer\n",
    "\n",
    "transformer = Normalizer().fit(train_X)\n",
    "train_X_normalized = transformer.transform(train_X)# 对训练集进行标准化\n",
    "test_X_normalized = transformer.transform(test_X)# 对测试集进行标准化\n",
    "\n",
    "train_X_normalized[-6:-1]\n",
    "test_X_normalized[-6:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "regr = linear_model.LogisticRegression()\n",
    "regr.fit(train_X_normalized, train_y) # 训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes', 'No', 'No', 'No', 'Yes', 'Yes'], dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_y = regr.predict(test_X)\n",
    "pred_y[:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 评价模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[150,  73],\n",
       "       [ 38,  71]], dtype=int64)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          No       0.80      0.67      0.73       223\n",
      "         Yes       0.49      0.65      0.56       109\n",
      "\n",
      "    accuracy                           0.67       332\n",
      "   macro avg       0.65      0.66      0.65       332\n",
      "weighted avg       0.70      0.67      0.67       332\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report, RocCurveDisplay\n",
    "confusion_matrix(test_y, pred_y)\n",
    "print(classification_report(test_y, pred_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:70: FutureWarning: Pass fpr=[0.         0.32735426 1.        ], tpr=[0.         0.65137615 1.        ], roc_auc=0.6620109433496523 as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error\n",
      "  warnings.warn(f\"Pass {args_msg} as keyword args. From version \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score, RocCurveDisplay\n",
    "\n",
    "auc = roc_auc_score(np.where(test_y == \"No\", 0, 1), np.where(pred_y == \"No\", 0, 1)) # AUC面积\n",
    "# 假阳性率，真阳性率\n",
    "fpr, tpr, thresholds = roc_curve(np.where(test_y == \"No\", 0, 1), np.where(pred_y == \"No\", 0, 1), pos_label=1)\n",
    "display = RocCurveDisplay(fpr, tpr, auc, pos_label=\"Yes\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5), dpi=100)\n",
    "display.plot(ax=ax)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "变量重要性:\n",
      " [[ 0.36030164  1.06526908 -1.85822375  0.11076152 -0.57760681  0.01257515\n",
      "   0.61407863]]\n",
      "正确率(Accuracy): 0.67\n",
      "准确率(Precision): 0.49\n",
      "灵敏度(Sensitivity)/召回率(recall): 0.65\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix\n",
    "\n",
    "print(\"变量重要性:\\n\", regr.coef_) # 绝对值越大，重要性越显著\n",
    "print(\"正确率(Accuracy): %.2f\" % accuracy_score(test_y, pred_y))\n",
    "print(\"准确率(Precision): %.2f\" % precision_score(test_y, pred_y, pos_label=\"Yes\"))\n",
    "print(\"灵敏度(Sensitivity)/召回率(recall): %.2f\" % recall_score(test_y, pred_y, pos_label=\"Yes\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**手动计算其它指标：**<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.672645739910314"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.3273542600896861"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.3486238532110092"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.6656626506024096"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.4930555555555556"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.6513761467889908"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(test_y, pred_y)\n",
    "tn = cm[0,0]\n",
    "tp = cm[1,1]\n",
    "fn = cm[1,0]\n",
    "fp = cm[0,1]\n",
    "\n",
    "tn/(tn + fp) # specificy, 特异度，真阴性率\n",
    "fp/(fp + tn) # fall out, 假阳性率\n",
    "fn/(fn + tp) # miss rate，假阴性率\n",
    "(tp + tn)/(tp + tn + fn + fp) # accuracy, 正确率\n",
    "tp/(tp + fp) # precision, 准确率\n",
    "tp/(tp + fn) # recall, 真阳性率，灵敏度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"color:blue; font-size:200%; font-weight:bold\">参考来源:</p>\n",
    "\n",
    "* [线性回归](https://www.w3school.com.cn/python/python_ml_linear_regression.asp)<br/>\n",
    "* [多项式回归](https://www.w3school.com.cn/python/python_ml_polynomial_regression.asp)<br/>\n",
    "* [sklearn.org](https://sklearn.org/)<br/>\n",
    "* [sklearn中文文档](https://sklearn.apachecn.org/)<br/>\n",
    "* [preprocessing模块](https://scikit-learn.org/stable/modules/preprocessing.html)<br/>\n",
    "* [impute模块](https://scikit-learn.org/stable/modules/impute.html#impute)<br/>\n",
    "* [user-guide](https://scikit-learn.org/stable/user_guide.html)<br/>"
   ]
  }
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